diff --git a/examples/cuda/sepconv_parallel.py b/examples/cuda/sepconv_parallel.py
new file mode 100644
index 00000000..074200e1
--- /dev/null
+++ b/examples/cuda/sepconv_parallel.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+import numpy
+from kernel_tuner import tune_kernel
+from collections import OrderedDict
+
+
+def tune():
+    with open("convolution.cu", "r") as f:
+        kernel_string = f.read()
+
+    # setup tunable parameters
+    tune_params = OrderedDict()
+    tune_params["filter_height"] = [i for i in range(3, 19, 2)]
+    tune_params["filter_width"] = [i for i in range(3, 19, 2)]
+    tune_params["block_size_x"] = [16 * i for i in range(1, 65)]
+    tune_params["block_size_y"] = [2**i for i in range(6)]
+    tune_params["tile_size_x"] = [i for i in range(1, 11)]
+    tune_params["tile_size_y"] = [i for i in range(1, 11)]
+
+    tune_params["use_padding"] = [0, 1]  # toggle the insertion of padding in shared memory
+    tune_params["read_only"] = [0, 1]  # toggle using the read-only cache
+
+    # limit the search to only use padding when its effective, and at least 32 threads in a block
+    restrict = ["use_padding==0 or (block_size_x % 32 != 0)", "block_size_x*block_size_y >= 32"]
+
+    # setup input and output dimensions
+    problem_size = (4096, 4096)
+    size = numpy.prod(problem_size)
+    largest_fh = max(tune_params["filter_height"])
+    largest_fw = max(tune_params["filter_width"])
+    input_size = (problem_size[0] + largest_fw - 1) * (problem_size[1] + largest_fh - 1)
+
+    # create input data
+    output_image = numpy.zeros(size).astype(numpy.float32)
+    input_image = numpy.random.randn(input_size).astype(numpy.float32)
+    filter_weights = numpy.random.randn(largest_fh * largest_fw).astype(numpy.float32)
+
+    # setup kernel arguments
+    cmem_args = {"d_filter": filter_weights}
+    args = [output_image, input_image, filter_weights]
+
+    # tell the Kernel Tuner how to compute grid dimensions
+    grid_div_x = ["block_size_x", "tile_size_x"]
+    grid_div_y = ["block_size_y", "tile_size_y"]
+
+    # start tuning separable convolution (row)
+    tune_params["filter_height"] = [1]
+    tune_params["tile_size_y"] = [1]
+    results_row = tune_kernel(
+        "convolution_kernel",
+        kernel_string,
+        problem_size,
+        args,
+        tune_params,
+        grid_div_y=grid_div_y,
+        grid_div_x=grid_div_x,
+        cmem_args=cmem_args,
+        verbose=False,
+        restrictions=restrict,
+        parallel_runner=1024,
+        cache="convolution_kernel_row",
+    )
+
+    # start tuning separable convolution (col)
+    tune_params["filter_height"] = tune_params["filter_width"][:]
+    tune_params["file_size_y"] = tune_params["tile_size_x"][:]
+    tune_params["filter_width"] = [1]
+    tune_params["tile_size_x"] = [1]
+    results_col = tune_kernel(
+        "convolution_kernel",
+        kernel_string,
+        problem_size,
+        args,
+        tune_params,
+        grid_div_y=grid_div_y,
+        grid_div_x=grid_div_x,
+        cmem_args=cmem_args,
+        verbose=False,
+        restrictions=restrict,
+        parallel_runner=1024,
+        cache="convolution_kernel_col",
+    )
+
+    return results_row, results_col
+
+
+if __name__ == "__main__":
+    results_row, results_col = tune()
diff --git a/examples/cuda/vector_add_parallel.py b/examples/cuda/vector_add_parallel.py
new file mode 100644
index 00000000..8d35ce7c
--- /dev/null
+++ b/examples/cuda/vector_add_parallel.py
@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+
+import numpy
+from kernel_tuner import tune_kernel
+
+
+def tune():
+    kernel_string = """
+    __global__ void vector_add(float *c, float *a, float *b, int n) {
+        int i = (blockIdx.x * block_size_x) + threadIdx.x;
+        if ( i < n ) {
+            c[i] = a[i] + b[i];
+        }
+    }
+    """
+
+    size = 10000000
+
+    a = numpy.random.randn(size).astype(numpy.float32)
+    b = numpy.random.randn(size).astype(numpy.float32)
+    c = numpy.zeros_like(b)
+    n = numpy.int32(size)
+
+    args = [c, a, b, n]
+
+    tune_params = dict()
+    tune_params["block_size_x"] = [32 * i for i in range(1, 33)]
+
+    results, env = tune_kernel("vector_add", kernel_string, size, args, tune_params, parallel_workers=True)
+    print(env)
+    return results
+
+
+if __name__ == "__main__":
+    tune()
diff --git a/kernel_tuner/backends/cupy.py b/kernel_tuner/backends/cupy.py
index 51613be7..87ba1514 100644
--- a/kernel_tuner/backends/cupy.py
+++ b/kernel_tuner/backends/cupy.py
@@ -73,6 +73,7 @@ def __init__(self, device=0, iterations=7, compiler_options=None, observers=None
             s.split(":")[0].strip(): s.split(":")[1].strip() for s in cupy_info
         }
         env["device_name"] = info_dict[f"Device {device} Name"]
+        env["pci_bus_id"] = info_dict[f"Device {device} PCI Bus ID"]
 
         env["cuda_version"] = cp.cuda.runtime.driverGetVersion()
         env["compute_capability"] = self.cc
diff --git a/kernel_tuner/backends/nvcuda.py b/kernel_tuner/backends/nvcuda.py
index 6729e683..c151e6a9 100644
--- a/kernel_tuner/backends/nvcuda.py
+++ b/kernel_tuner/backends/nvcuda.py
@@ -100,6 +100,7 @@ def __init__(self, device=0, iterations=7, compiler_options=None, observers=None
         cuda_error_check(err)
         env = dict()
         env["device_name"] = device_properties.name.decode()
+        env["pci_bus_id"] = device_properties.pciBusID
         env["cuda_version"] = driver.CUDA_VERSION
         env["compute_capability"] = self.cc
         env["iterations"] = self.iterations
diff --git a/kernel_tuner/backends/pycuda.py b/kernel_tuner/backends/pycuda.py
index c8f3e689..8f9326c2 100644
--- a/kernel_tuner/backends/pycuda.py
+++ b/kernel_tuner/backends/pycuda.py
@@ -139,6 +139,7 @@ def _finish_up():
         # collect environment information
         env = dict()
         env["device_name"] = self.context.get_device().name()
+        env["pci_bus_id"] = self.context.get_device().pci_bus_id()
         env["cuda_version"] = ".".join([str(i) for i in drv.get_version()])
         env["compute_capability"] = self.cc
         env["iterations"] = self.iterations
diff --git a/kernel_tuner/interface.py b/kernel_tuner/interface.py
index 0641eb7e..b05fd535 100644
--- a/kernel_tuner/interface.py
+++ b/kernel_tuner/interface.py
@@ -39,8 +39,6 @@
 import kernel_tuner.util as util
 from kernel_tuner.file_utils import get_input_file, get_t4_metadata, get_t4_results, import_class_from_file
 from kernel_tuner.integration import get_objective_defaults
-from kernel_tuner.runners.sequential import SequentialRunner
-from kernel_tuner.runners.simulation import SimulationRunner
 from kernel_tuner.searchspace import Searchspace
 
 try:
@@ -476,6 +474,7 @@ def __deepcopy__(self, _):
         ),
         ("metrics", ("specifies user-defined metrics, please see :ref:`metrics`.", "dict")),
         ("simulation_mode", ("Simulate an auto-tuning search from an existing cachefile", "bool")),
+        ("parallel_workers", ("Set to `True` or an integer to enable parallel tuning. If set to an integer, this will be the number of parallel workers.", "int|bool")),
         ("observers", ("""A list of Observers to use during tuning, please see :ref:`observers`.""", "list")),
     ]
 )
@@ -587,6 +586,7 @@ def tune_kernel(
     cache=None,
     metrics=None,
     simulation_mode=False,
+    parallel_workers=None,
     observers=None,
     objective=None,
     objective_higher_is_better=None,
@@ -616,18 +616,23 @@ def tune_kernel(
     kernel_options = Options([(k, opts[k]) for k in _kernel_options.keys()])
     tuning_options = Options([(k, opts[k]) for k in _tuning_options.keys()])
     device_options = Options([(k, opts[k]) for k in _device_options.keys()])
-    tuning_options["unique_results"] = {}
 
     # copy some values from strategy_options
     searchspace_construction_options = {}
+    max_fevals = None
+    time_limit = None
+
     if strategy_options:
         if "max_fevals" in strategy_options:
-            tuning_options["max_fevals"] = strategy_options["max_fevals"]
+            max_fevals = strategy_options["max_fevals"]
+            tuning_options["max_fevals"] = max_fevals  # TODO: Is this used?
         if "time_limit" in strategy_options:
-            tuning_options["time_limit"] = strategy_options["time_limit"] 
+            time_limit = strategy_options["time_limit"] 
+            tuning_options["time_limit"] = time_limit  # TODO: Is this used?
         if "searchspace_construction_options" in strategy_options:
             searchspace_construction_options = strategy_options["searchspace_construction_options"]         
 
+
     # log the user inputs
     logging.debug("tune_kernel called")
     logging.debug("kernel_options: %s", util.get_config_string(kernel_options))
@@ -654,9 +659,22 @@ def tune_kernel(
         strategy = brute_force
 
     # select the runner for this job based on input
-    selected_runner = SimulationRunner if simulation_mode else SequentialRunner
+    # TODO: we could use the "match case" syntax when removing support for 3.9
     tuning_options.simulated_time = 0
-    runner = selected_runner(kernelsource, kernel_options, device_options, iterations, observers)
+
+    if parallel_workers and simulation_mode:
+        raise ValueError("Enabling `parallel_workers` and `simulation_mode` together is not supported")
+    elif simulation_mode:
+        from kernel_tuner.runners.simulation import SimulationRunner
+        runner = SimulationRunner(kernelsource, kernel_options, device_options, iterations, observers)
+    elif parallel_workers:
+        from kernel_tuner.runners.parallel import ParallelRunner
+        num_workers = None if parallel_workers is True else parallel_workers
+        runner = ParallelRunner(kernelsource, kernel_options, device_options, tuning_options, iterations, observers, num_workers=num_workers)
+    else:
+        from kernel_tuner.runners.sequential import SequentialRunner
+        runner = SequentialRunner(kernelsource, kernel_options, device_options, iterations, observers)
+
 
     # the user-specified function may or may not have an optional atol argument;
     # we normalize it so that it always accepts atol.
@@ -672,33 +690,50 @@ def preprocess_cache(filepath):
     # process cache
     if cache:
         cache = preprocess_cache(cache)
-        util.process_cache(cache, kernel_options, tuning_options, runner)
+        tuning_options.cachefile = cache
+        tuning_options.cache = util.process_cache(cache, kernel_options, tuning_options, runner)
     else:
-        tuning_options.cache = {}
         tuning_options.cachefile = None
+        tuning_options.cache = {}
 
     # create search space
     tuning_options.restrictions_unmodified = deepcopy(restrictions)
-    searchspace = Searchspace(tune_params, restrictions, runner.dev.max_threads, **searchspace_construction_options)
+    device_info = runner.get_device_info()
+    searchspace = Searchspace(tune_params, restrictions, device_info.max_threads, **searchspace_construction_options)
+    
     restrictions = searchspace._modified_restrictions
     tuning_options.restrictions = restrictions
+
     if verbose:
         print(f"Searchspace has {searchspace.size} configurations after restrictions.")
 
     # register the times and raise an exception if the budget is exceeded
-    if "time_limit" in tuning_options:
-        tuning_options["startup_time"] = perf_counter() - start_overhead_time
-        if tuning_options["startup_time"] > tuning_options["time_limit"]:
+    startup_time = perf_counter() - start_overhead_time
+
+    if time_limit is not None:
+        if startup_time > time_limit:
             raise RuntimeError(
-                f"The startup time of the tuning process ({tuning_options['startup_time']} seconds) has exceeded the time limit ({tuning_options['time_limit']} seconds). "
+                f"The startup time of the tuning process ({startup_time} seconds) has exceeded the time limit ({time_limit} seconds). "
                 "Please increase the time limit or decrease the size of the search space."
             )
-    tuning_options["start_time"] = perf_counter()
+
+        time_limit -= startup_time
+
+    if max_fevals is None or max_fevals > searchspace.size:
+        logging.info(f"evaluation limit has been adjusted from {max_fevals} to {searchspace.size} (search space size)")
+        max_fevals = searchspace.size
+
+    # Create the budget
+    tuning_options["budget"] = util.TuningBudget(time_limit, max_fevals)
+
 
     # call the strategy to execute the tuning process
     results = strategy.tune(searchspace, runner, tuning_options)
     env = runner.get_environment(tuning_options)
 
+    # Shut down the runner
+    runner.shutdown()
+
     # finished iterating over search space
     if results:  # checks if results is not empty
         best_config = util.get_best_config(results, objective, objective_higher_is_better)
diff --git a/kernel_tuner/runners/parallel.py b/kernel_tuner/runners/parallel.py
new file mode 100644
index 00000000..0a8edcf8
--- /dev/null
+++ b/kernel_tuner/runners/parallel.py
@@ -0,0 +1,355 @@
+"""A specialized runner that tunes in parallel the parameter space."""
+from collections import deque
+import logging
+import socket
+from time import perf_counter
+from typing import List, Optional
+from kernel_tuner.core import DeviceInterface
+from kernel_tuner.interface import Options
+from kernel_tuner.runners.runner import Runner
+from kernel_tuner.util import (
+    BudgetExceededConfig,
+    ErrorConfig,
+    TuningBudget,
+    print_config_output,
+    process_metrics,
+    store_cache,
+)
+from datetime import datetime, timezone
+
+logger = logging.getLogger(__name__)
+
+try:
+    import ray
+except ImportError as e:
+    raise ImportError(f"unable to initialize the parallel runner: {e}") from e
+
+
+@ray.remote(num_gpus=1)
+class DeviceActor:
+    def __init__(
+        self, kernel_source, kernel_options, device_options, tuning_options, iterations, observers
+    ):
+        # detect language and create high-level device interface
+        self.dev = DeviceInterface(
+            kernel_source, iterations=iterations, observers=observers, **device_options
+        )
+
+        self.units = self.dev.units
+        self.quiet = device_options.quiet
+        self.kernel_source = kernel_source
+        self.warmed_up = False if self.dev.requires_warmup else True
+        self.kernel_options = kernel_options
+        self.tuning_options = tuning_options
+
+        # move data to the GPU
+        self.gpu_args = self.dev.ready_argument_list(kernel_options.arguments)
+
+    def shutdown(self):
+        ray.actor.exit_actor()
+
+    def get_environment(self):
+        # Get the device properties
+        env = dict(self.dev.get_environment())
+
+        # Get the host name
+        env["host_name"] = socket.gethostname()
+
+        # Get info about the ray instance
+        ctx = ray.get_runtime_context()
+        env["ray"] = {
+            "node_id": ctx.get_node_id(),
+            "worker_id": ctx.get_worker_id(),
+            "actor_id": ctx.get_actor_id(),
+        }
+
+        return env
+
+    def run(self, key, element):
+        # TODO: logging.debug("sequential runner started for " + self.kernel_options.kernel_name)
+        params = dict(element)
+        result = None
+        warmup_time = 0
+
+        # attempt to warmup the GPU by running the first config in the parameter space and ignoring the result
+        if not self.warmed_up:
+            warmup_time = perf_counter()
+            self.dev.compile_and_benchmark(
+                self.kernel_source, self.gpu_args, params, self.kernel_options, self.tuning_options
+            )
+            self.warmed_up = True
+            warmup_time = 1e3 * (perf_counter() - warmup_time)
+
+        result = self.dev.compile_and_benchmark(
+            self.kernel_source, self.gpu_args, params, self.kernel_options, self.tuning_options
+        )
+
+        params.update(result)
+
+        params["timestamp"] = datetime.now(timezone.utc).isoformat()
+        params["ray_actor_id"] = ray.get_runtime_context().get_actor_id()
+        params["host_name"] = socket.gethostname()
+
+        # all visited configurations are added to results to provide a trace for optimization strategies
+        return key, params
+
+
+class DeviceActorState:
+    def __init__(self, index, actor):
+        self.index = index
+        self.actor = actor
+        self.running_jobs = []
+        self.maximum_running_jobs = 1
+        self.is_running = True
+        self.env = ray.get(actor.get_environment.remote())
+
+    def __repr__(self):
+        actor_id = self.env["ray"]["actor_id"]
+        host_name = self.env["host_name"]
+        return f"{self.index} ({host_name}, {actor_id})"
+
+    def shutdown(self):
+        if not self.is_running:
+            return
+
+        self.is_running = False
+
+        try:
+            self.actor.shutdown.remote()
+        except Exception:
+            logger.exception("Failed to request actor shutdown: %s", self)
+
+    def submit(self, key, config):
+        logger.info(f"job submitted to worker {self}: {key}")
+        job = self.actor.run.remote(key, config)
+        self.running_jobs.append(job)
+        return job
+
+    def is_available(self):
+        if not self.is_running:
+            return False
+
+        # Check for ready jobs, but do not block
+        ready_jobs, self.running_jobs = ray.wait(self.running_jobs, timeout=0)
+
+        for job in ready_jobs:
+            try:
+                key, _result = ray.get(job)
+                logger.info(f"job finished on worker {self}: {key}")
+            except Exception:
+                logger.exception(f"job failed on worker {self}")
+
+        # Available if this actor can now run another job
+        return len(self.running_jobs) < self.maximum_running_jobs
+
+
+class ParallelRunner(Runner):
+    def __init__(
+        self,
+        kernel_source,
+        kernel_options,
+        device_options,
+        tuning_options,
+        iterations,
+        observers,
+        num_workers=None,
+    ):
+        if not ray.is_initialized():
+            ray.init()
+
+        if num_workers is None:
+            num_workers = int(ray.cluster_resources().get("GPU", 0))
+
+        if num_workers == 0:
+            raise RuntimeError("failed to initialize parallel runner: no GPUs found")
+
+        if num_workers < 1:
+            raise RuntimeError(
+                f"failed to initialize parallel runner: invalid number of GPUs specified: {num_workers}"
+            )
+
+        self.workers = []
+
+        try:
+            # Start workers
+            for index in range(num_workers):
+                actor = DeviceActor.remote(
+                    kernel_source,
+                    kernel_options,
+                    device_options,
+                    tuning_options,
+                    iterations,
+                    observers,
+                )
+                worker = DeviceActorState(index, actor)
+                self.workers.append(worker)
+
+                logger.info(f"connected to worker {worker}")
+
+            # Check if all workers have the same device
+            device_names = {w.env.get("device_name") for w in self.workers}
+            if len(device_names) != 1:
+                raise RuntimeError(
+                    f"failed to initialize parallel runner: workers have different devices: {sorted(device_names)}"
+                )
+        except:
+            # If an exception occurs, shut down the worker and reraise error
+            self.shutdown()
+            raise
+
+        self.device_name = device_names.pop()
+
+        # TODO: Get units from the device?
+        self.start_time = perf_counter()
+        self.units = {"time": "ms"}
+        self.quiet = device_options.quiet
+
+    def get_device_info(self):
+        # TODO: Get this from the device?
+        return Options({"max_threads": 1024})
+
+    def get_environment(self, tuning_options):
+        return {"device_name": self.device_name, "workers": [w.env for w in self.workers]}
+
+    def shutdown(self):
+        for worker in self.workers:
+            try:
+                worker.shutdown()
+            except Exception as err:
+                logger.exception(f"error while shutting down worker {worker}")
+
+    def available_parallelism(self):
+        return len(self.workers)
+
+    def submit_jobs(self, jobs, budget: TuningBudget):
+        pending_jobs = deque(jobs)
+        running_jobs = []
+
+        while pending_jobs and not budget.is_done():
+            job_was_submitted = False
+
+            # If there is still work left, submit it now
+            for i, worker in enumerate(list(self.workers)):
+                if worker.is_available():
+                    # Push worker to back of list
+                    self.workers.pop(i)
+                    self.workers.append(worker)
+
+                    # Pop job and submit it
+                    key, config = pending_jobs.popleft()
+                    ref = worker.submit(key, config)
+                    running_jobs.append(ref)
+
+                    job_was_submitted = True
+                    budget.add_evaluations(1)
+                    break
+
+            # If no work was submitted, wait until a worker is available
+            if not job_was_submitted:
+                if not running_jobs:
+                    raise RuntimeError("invalid state: no ray workers available")
+
+                ready_jobs, running_jobs = ray.wait(running_jobs, num_returns=1)
+
+                for result in ready_jobs:
+                    yield ray.get(result)
+
+        # If there are still pending jobs, then the budget has been exceeded.
+        # We return `None` to indicate that no result is available for these jobs.
+        while pending_jobs:
+            key, _ = pending_jobs.popleft()
+            yield (key, None)
+
+        # Wait until running jobs complete
+        while running_jobs:
+            ready_jobs, running_jobs = ray.wait(running_jobs, num_returns=1)
+
+            for result in ready_jobs:
+                yield ray.get(result)
+
+    def run(self, parameter_space, tuning_options) -> List[Optional[dict]]:
+        metrics = tuning_options.metrics
+        objective = tuning_options.objective
+
+        jobs = []  # Jobs that need to be executed
+        results = []  # Results that will be returned at the end
+        key2index = dict()  # Used to insert job result back into `results`
+
+        total_worker_time = 0
+
+        # Select jobs which are not in the cache
+        for index, config in enumerate(parameter_space):
+            params = dict(zip(tuning_options.tune_params.keys(), config))
+            key = ",".join([str(i) for i in config])
+
+            if key in tuning_options.cache:
+                params.update(tuning_options.cache[key])
+
+                # Simulate compile, verification, and benchmark time
+                tuning_options.budget.add_time(milliseconds=params["compile_time"])
+                tuning_options.budget.add_time(milliseconds=params["verification_time"])
+                tuning_options.budget.add_time(milliseconds=params["benchmark_time"])
+                results.append(params)
+            else:
+                assert key not in key2index, "duplicate jobs submitted"
+                key2index[key] = index
+
+                jobs.append((key, params))
+                results.append(None)
+
+
+        # Submit jobs and wait for them to finish
+        for key, result in self.submit_jobs(jobs, tuning_options.budget):
+            # `None` indicate that no result is available since the budget is exceeded.
+            # We can skip it, meaning that `results` contains `None`s for these entries
+            if result is None:
+                continue
+
+            # Store the result into the output array
+            results[key2index[key]] = result
+
+            # Collect total time spent by worker
+            total_worker_time += (
+                result["compile_time"] + result["verification_time"] + result["benchmark_time"]
+            )
+
+            if isinstance(result.get(objective), ErrorConfig):
+                logging.error(
+                    "kernel configuration {key} was skipped silently due to compile or runtime failure",
+                    key,
+                )
+
+            # print configuration to the console
+            print_config_output(
+                tuning_options.tune_params, result, self.quiet, tuning_options.metrics, self.units
+            )
+
+            # add configuration to cache
+            store_cache(key, result, tuning_options.cachefile, tuning_options.cache)
+
+        total_time = 1000 * (perf_counter() - self.start_time)
+        self.start_time = perf_counter()
+
+        strategy_time = self.last_strategy_time
+        self.last_strategy_time = 0
+
+        runner_time = total_time - strategy_time
+        framework_time = max(runner_time * len(self.workers) - total_worker_time, 0)
+
+        num_valid_results = sum(bool(r) for r in results) # Count the number of valid results
+
+        # Post-process all the results
+        for result in results:
+            # Skip missing results
+            if not result:
+                continue
+
+            # Amortize the time over all the results
+            result["strategy_time"] = strategy_time / num_valid_results
+            result["framework_time"] = framework_time / num_valid_results
+
+            # only compute metrics on configs that have not errored
+            if not isinstance(result.get(objective), ErrorConfig):
+                result = process_metrics(result, metrics)
+
+        return results
diff --git a/kernel_tuner/runners/runner.py b/kernel_tuner/runners/runner.py
index 80ab3214..e95b7811 100644
--- a/kernel_tuner/runners/runner.py
+++ b/kernel_tuner/runners/runner.py
@@ -13,8 +13,19 @@ def __init__(
     ):
         pass
 
+    def shutdown(self):
+        pass
+
+    def available_parallelism(self):
+        """ Gives an indication of how many jobs this runner can execute in parallel. """
+        return 1
+
+    @abstractmethod
+    def get_device_info(self):
+        pass
+
     @abstractmethod
-    def get_environment(self):
+    def get_environment(self, tuning_options):
         pass
 
     @abstractmethod
diff --git a/kernel_tuner/runners/sequential.py b/kernel_tuner/runners/sequential.py
index 5e53093b..1814cefe 100644
--- a/kernel_tuner/runners/sequential.py
+++ b/kernel_tuner/runners/sequential.py
@@ -20,15 +20,13 @@ def __init__(self, kernel_source, kernel_options, device_options, iterations, ob
         :param kernel_options: A dictionary with all options for the kernel.
         :type kernel_options: kernel_tuner.interface.Options
 
-        :param device_options: A dictionary with all options for the device
-            on which the kernel should be tuned.
+        :param device_options: A dictionary with all options for the device on which the kernel should be tuned.
         :type device_options: kernel_tuner.interface.Options
 
-        :param iterations: The number of iterations used for benchmarking
-            each kernel instance.
+        :param iterations: The number of iterations used for benchmarking each kernel instance.
         :type iterations: int
         """
-        #detect language and create high-level device interface
+        # detect language and create high-level device interface
         self.dev = DeviceInterface(kernel_source, iterations=iterations, observers=observers, **device_options)
 
         self.units = self.dev.units
@@ -41,9 +39,12 @@ def __init__(self, kernel_source, kernel_options, device_options, iterations, ob
         self.last_strategy_time = 0
         self.kernel_options = kernel_options
 
-        #move data to the GPU
+        # move data to the GPU
         self.gpu_args = self.dev.ready_argument_list(kernel_options.arguments)
 
+    def get_device_info(self):
+        return self.dev
+
     def get_environment(self, tuning_options):
         return self.dev.get_environment()
 
@@ -53,21 +54,29 @@ def run(self, parameter_space, tuning_options):
         :param parameter_space: The parameter space as an iterable.
         :type parameter_space: iterable
 
-        :param tuning_options: A dictionary with all options regarding the tuning
-            process.
-        :type tuning_options: kernel_tuner.iterface.Options
+        :param tuning_options: A dictionary with all options regarding the tuning process.
+        :type tuning_options: kernel_tuner.interface.Options
 
-        :returns: A list of dictionaries for executed kernel configurations and their
-            execution times.
-        :rtype: dict())
+        :returns: A list of dictionaries for executed kernel configurations and their execution times.
+        :rtype: dict()
 
         """
-        logging.debug('sequential runner started for ' + self.kernel_options.kernel_name)
+        logging.debug("sequential runner started for " + self.kernel_options.kernel_name)
 
         results = []
 
+        # self.last_strategy_time is set by cost_func
+        strategy_time_per_config = self.last_strategy_time / len(parameter_space) if len(parameter_space) > 0 else 0
+
         # iterate over parameter space
         for element in parameter_space:
+            # If the time limit is exceeded, just skip this element. Add `None` to
+            # indicate to CostFunc that no result is available for this config.
+            if tuning_options.budget.is_done():
+                results.append(None)
+                continue
+            
+            tuning_options.budget.add_evaluations(1)
             params = dict(zip(tuning_options.tune_params.keys(), element))
 
             result = None
@@ -77,33 +86,47 @@ def run(self, parameter_space, tuning_options):
             x_int = ",".join([str(i) for i in element])
             if tuning_options.cache and x_int in tuning_options.cache:
                 params.update(tuning_options.cache[x_int])
-                params['compile_time'] = 0
-                params['verification_time'] = 0
-                params['benchmark_time'] = 0
+
+                # Simulate compile, verification, and benchmark time
+                tuning_options.budget.add_time(milliseconds=params["compile_time"])
+                tuning_options.budget.add_time(milliseconds=params["verification_time"])
+                tuning_options.budget.add_time(milliseconds=params["benchmark_time"])
             else:
                 # attempt to warmup the GPU by running the first config in the parameter space and ignoring the result
                 if not self.warmed_up:
                     warmup_time = perf_counter()
-                    self.dev.compile_and_benchmark(self.kernel_source, self.gpu_args, params, self.kernel_options, tuning_options)
+                    self.dev.compile_and_benchmark(
+                        self.kernel_source, self.gpu_args, params, self.kernel_options, tuning_options
+                    )
                     self.warmed_up = True
                     warmup_time = 1e3 * (perf_counter() - warmup_time)
 
-                result = self.dev.compile_and_benchmark(self.kernel_source, self.gpu_args, params, self.kernel_options, tuning_options)
+                result = self.dev.compile_and_benchmark(
+                    self.kernel_source, self.gpu_args, params, self.kernel_options, tuning_options
+                )
 
                 params.update(result)
 
                 if tuning_options.objective in result and isinstance(result[tuning_options.objective], ErrorConfig):
-                    logging.debug('kernel configuration was skipped silently due to compile or runtime failure')
+                    logging.debug("kernel configuration was skipped silently due to compile or runtime failure")
 
             # only compute metrics on configs that have not errored
             if tuning_options.metrics and not isinstance(params.get(tuning_options.objective), ErrorConfig):
                 params = process_metrics(params, tuning_options.metrics)
 
             # get the framework time by estimating based on other times
-            total_time = 1000 * ((perf_counter() - self.start_time) - warmup_time) 
-            params['strategy_time'] = self.last_strategy_time
-            params['framework_time'] = max(total_time - (params['compile_time'] + params['verification_time'] + params['benchmark_time'] + params['strategy_time']), 0)
-            params['timestamp'] = str(datetime.now(timezone.utc))
+            total_time = 1000 * (perf_counter() - self.start_time) - warmup_time
+            params["strategy_time"] = strategy_time_per_config
+            params["framework_time"] = max(
+                total_time
+                - (
+                    params["compile_time"]
+                    + params["verification_time"]
+                    + params["benchmark_time"]
+                ),
+                0,
+            )
+            params["timestamp"] = str(datetime.now(timezone.utc))
             self.start_time = perf_counter()
 
             if result:
@@ -111,7 +134,7 @@ def run(self, parameter_space, tuning_options):
                 print_config_output(tuning_options.tune_params, params, self.quiet, tuning_options.metrics, self.units)
 
                 # add configuration to cache
-                store_cache(x_int, params, tuning_options)
+                store_cache(x_int, params, tuning_options.cachefile, tuning_options.cache)
 
             # all visited configurations are added to results to provide a trace for optimization strategies
             results.append(params)
diff --git a/kernel_tuner/runners/simulation.py b/kernel_tuner/runners/simulation.py
index 9695879d..0c0affd7 100644
--- a/kernel_tuner/runners/simulation.py
+++ b/kernel_tuner/runners/simulation.py
@@ -16,11 +16,11 @@ class SimulationDevice(_SimulationDevice):
 
     @property
     def name(self):
-        return self.env['device_name']
+        return self.env["device_name"]
 
     @name.setter
     def name(self, value):
-        self.env['device_name'] = value
+        self.env["device_name"] = value
         if not self.quiet:
             print("Simulating: " + value)
 
@@ -40,12 +40,10 @@ def __init__(self, kernel_source, kernel_options, device_options, iterations, ob
         :param kernel_options: A dictionary with all options for the kernel.
         :type kernel_options: kernel_tuner.interface.Options
 
-        :param device_options: A dictionary with all options for the device
-            on which the kernel should be tuned.
+        :param device_options: A dictionary with all options for the device on which the kernel should be tuned.
         :type device_options: kernel_tuner.interface.Options
 
-        :param iterations: The number of iterations used for benchmarking
-            each kernel instance.
+        :param iterations: The number of iterations used for benchmarking each kernel instance.
         :type iterations: int
         """
         self.quiet = device_options.quiet
@@ -56,14 +54,18 @@ def __init__(self, kernel_source, kernel_options, device_options, iterations, ob
         self.kernel_options = kernel_options
 
         self.start_time = perf_counter()
+        self.total_simulated_time = 0
         self.last_strategy_start_time = self.start_time
         self.last_strategy_time = 0
         self.units = {}
 
+    def get_device_info(self):
+        return self.dev
+    
     def get_environment(self, tuning_options):
         env = self.dev.get_environment()
         env["simulation"] = True
-        env["simulated_time"] = tuning_options.simulated_time
+        env["simulated_time"] = self.total_simulated_time
         return env
 
     def run(self, parameter_space, tuning_options):
@@ -72,64 +74,62 @@ def run(self, parameter_space, tuning_options):
         :param parameter_space: The parameter space as an iterable.
         :type parameter_space: iterable
 
-        :param tuning_options: A dictionary with all options regarding the tuning
-            process.
+        :param tuning_options: A dictionary with all options regarding the tuning process.
         :type tuning_options: kernel_tuner.iterface.Options
 
-        :returns: A list of dictionaries for executed kernel configurations and their
-            execution times.
+        :returns: A list of dictionaries for executed kernel configurations and their execution times.
         :rtype: dict()
         """
-        logging.debug('simulation runner started for ' + self.kernel_options.kernel_name)
+        logging.debug("simulation runner started for " + self.kernel_options.kernel_name)
 
         results = []
 
-        # iterate over parameter space 
+        # self.last_strategy_time is set by cost_func
+        strategy_time_per_config = self.last_strategy_time / len(parameter_space) if len(parameter_space) > 0 else 0
+
+        # iterate over parameter space
         for element in parameter_space:
 
+            # Append `None` to indicate that the tuning budget has been exceeded
+            if tuning_options.budget.is_done():
+                results.append(None)
+                continue
+            
             # check if element is in the cache
-            x_int = ",".join([str(i) for i in element])
-            if tuning_options.cache and x_int in tuning_options.cache:
-                result = tuning_options.cache[x_int].copy()
+            key = ",".join([str(i) for i in element])
+
+            if key in tuning_options.cache:
+                # Get from cache and create a copy
+                result = dict(tuning_options.cache[key])
 
                 # only compute metrics on configs that have not errored
                 if tuning_options.metrics and not isinstance(result.get(tuning_options.objective), util.ErrorConfig):
                     result = util.process_metrics(result, tuning_options.metrics)
 
-
-                # Simulate behavior of sequential runner that when a configuration is
-                # served from the cache by the sequential runner, the compile_time,
-                # verification_time, and benchmark_time are set to 0.
-                # This step is only performed in the simulation runner when a configuration
-                # is served from the cache beyond the first timel. That is, when the
-                # configuration is already counted towards the unique_results.
-                # It is the responsibility of cost_func to add configs to unique_results.
-                if x_int in tuning_options.unique_results:
-
-                    result['compile_time'] = 0
-                    result['verification_time'] = 0
-                    result['benchmark_time'] = 0
-
-                else:
-                    # configuration is evaluated for the first time, print to the console
-                    util.print_config_output(tuning_options.tune_params, result, self.quiet, tuning_options.metrics, self.units)
+                # configuration is evaluated for the first time, print to the console
+                util.print_config_output(
+                    tuning_options.tune_params, result, self.quiet, tuning_options.metrics, self.units
+                )
 
                 # Everything but the strategy time and framework time are simulated,
-                # self.last_strategy_time is set by cost_func
-                result['strategy_time'] = self.last_strategy_time
+                result["strategy_time"] = strategy_time_per_config
+
+                # Simulate the evaluation of this configuration
+                tuning_options.budget.add_evaluations(1)
+                tuning_options.budget.add_time(milliseconds=result["compile_time"])
+                tuning_options.budget.add_time(milliseconds=result["verification_time"])
+                tuning_options.budget.add_time(milliseconds=result["benchmark_time"])
 
                 try:
-                    simulated_time = result['compile_time'] + result['verification_time'] + result['benchmark_time']
-                    tuning_options.simulated_time += simulated_time
+                    self.total_simulated_time += result["compile_time"] + result["verification_time"] + result["benchmark_time"]
                 except KeyError:
-                    if "time_limit" in tuning_options:
-                        raise RuntimeError(
-                            "Cannot use simulation mode with a time limit on a cache file that does not have full compile, verification, and benchmark timings on all configurations"
-                        )
+                    raise RuntimeError(
+                        "Cannot use simulation mode with a time limit on a cache file that does not have full compile, verification, and benchmark timings on all configurations"
+                    )
 
                 total_time = 1000 * (perf_counter() - self.start_time)
                 self.start_time = perf_counter()
-                result['framework_time'] = total_time - self.last_strategy_time
+                result["framework_time"] = total_time
 
                 results.append(result)
                 continue
@@ -142,11 +142,11 @@ def run(self, parameter_space, tuning_options):
                 result['compile_time'] = 0
                 result['verification_time'] = 0
                 result['benchmark_time'] = 0
-                result['strategy_time'] = self.last_strategy_time
+                result['strategy_time'] = strategy_time_per_config
 
                 total_time = 1000 * (perf_counter() - self.start_time)
                 self.start_time = perf_counter()
-                result['framework_time'] = total_time - self.last_strategy_time
+                result['framework_time'] = total_time
 
                 result[tuning_options.objective] = util.InvalidConfig()
                 results.append(result)
diff --git a/kernel_tuner/strategies/bayes_opt.py b/kernel_tuner/strategies/bayes_opt.py
index a814e7ce..64d4c623 100644
--- a/kernel_tuner/strategies/bayes_opt.py
+++ b/kernel_tuner/strategies/bayes_opt.py
@@ -455,6 +455,33 @@ def fit_observations_to_model(self):
         """Update the model based on the current list of observations."""
         self.__model.fit(self.__valid_params, self.__valid_observations)
 
+    def evaluate_parallel_objective_function(self, param_configs: list[tuple]) -> list[float]:
+        """Evaluates the objective function for multiple configurations in parallel."""
+        results = []
+        valid_param_configs = []
+        valid_indices = []
+
+        # Extract the valid configurations
+        for param_config in param_configs:
+            param_config = self.unprune_param_config(param_config)
+            denormalized_param_config = self.denormalize_param_config(param_config)
+            if not self.__searchspace_obj.is_param_config_valid(denormalized_param_config):
+                results.append(self.invalid_value)
+            else:
+                valid_indices.append(len(results))
+                results.append(None)
+                valid_param_configs.append(param_config)
+
+        # Run valid configurations in parallel
+        scores = self.cost_func.eval_all(valid_param_configs)
+
+        # Put the scores at the right location in the result
+        for idx, score in zip(valid_indices, scores):
+            results[idx] = score
+            
+        self.fevals += len(scores)
+        return results
+
     def evaluate_objective_function(self, param_config: tuple) -> float:
         """Evaluates the objective function."""
         param_config = self.unprune_param_config(param_config)
diff --git a/kernel_tuner/strategies/common.py b/kernel_tuner/strategies/common.py
index b51274ce..0964597d 100644
--- a/kernel_tuner/strategies/common.py
+++ b/kernel_tuner/strategies/common.py
@@ -84,102 +84,174 @@ def __init__(
             scaling: whether to internally scale parameter values. Defaults to False.
             snap: whether to snap given configurations to their closests equivalent in the space. Defaults to True.
             return_invalid: whether to return the util.ErrorConfig of an invalid configuration. Defaults to False.
-            return_raw: returns (result, results[raw]). Key inferred from objective if set to True. Defaults to None.
         """
         self.searchspace = searchspace
         self.tuning_options = tuning_options
-        if isinstance(self.tuning_options, dict):
-            self.tuning_options["max_fevals"] = min(
-                tuning_options["max_fevals"] if "max_fevals" in tuning_options else np.inf, searchspace.size
-            )
+        self.objective = tuning_options.objective
+        self.objective_higher_is_better = tuning_options.objective_higher_is_better
+        self.constraint_aware = bool(tuning_options.strategy_options.get("constraint_aware"))
         self.runner = runner
         self.scaling = scaling
         self.snap = snap
         self.return_invalid = return_invalid
-        self.return_raw = return_raw
-        if return_raw is True:
-            self.return_raw = f"{tuning_options['objective']}s"
+        self.unique_results = dict()
         self.results = []
         self.budget_spent_fraction = 0.0
         self.invalid_return_value = invalid_value
 
+    def _normalize_and_validate_config(self, x, check_restrictions=True):
+        # snap values in x to nearest actual value for each parameter, unscale x if needed
+        if self.snap:
+            if self.scaling:
+                config = unscale_and_snap_to_nearest(x, self.searchspace.tune_params, self.tuning_options.eps)
+            else:
+                config = snap_to_nearest_config(x, self.searchspace.tune_params)
+        else:
+            config = x
 
-    def __call__(self, x, check_restrictions=True):
-        """Cost function used by almost all strategies."""
+        is_legal = True
+
+        # else check if this is a legal (non-restricted) configuration
+        if check_restrictions:
+            is_legal = self.searchspace.is_param_config_valid(tuple(config))
+
+        # Attempt to repare the config
+        if not is_legal and self.constraint_aware:
+            # attempt to repair
+            new_config = unscale_and_snap_to_nearest_valid(x, config, self.searchspace, self.tuning_options.eps)
+
+            if new_config:
+                config = new_config
+                is_legal = True
+
+        return config, is_legal
+
+
+    def _run_configs(self, xs, check_restrictions=True):
+        """ Takes a list of Euclidian coordinates and evaluates the configurations at those points. """
         self.runner.last_strategy_time = 1000 * (perf_counter() - self.runner.last_strategy_start_time)
+        self.runner.start_time = perf_counter() # start framework time
 
         # error value to return for numeric optimizers that need a numerical value
         logging.debug("_cost_func called")
-        logging.debug("x: %s", str(x))
 
         # check if max_fevals is reached or time limit is exceeded
-        self.budget_spent_fraction = util.check_stop_criterion(self.tuning_options)
+        self.tuning_options.budget.raise_exception_if_done()
+
+        batch_configs = []  # The configs to run
+        batch_keys = [] # The keys of the configs to run
+        pending_indices_by_key = dict()  # Maps key => where to store result in `final_results`
+        final_results = []  # List returned to the user
+        legal_indices = []  # Indices in `final_results` that are legal
+
+        # Loop over all configurations. For each configurations there are four cases:
+        # 1. The configuration is invalid, we can skip it
+        # 2. The configuration is in  `unique_results`, we can get it from there
+        # 3. The configuration is in  `pending_indices_by_key`, it is duplicate in `xs`
+        # 4. The configuration must be evaluated by the runner.
+        for index, x in enumerate(xs):
+            config, is_legal = self._normalize_and_validate_config(x, check_restrictions=check_restrictions)
+            logging.debug("normalize config: %s -> %s (legal: %s)", str(x), str(config), is_legal)
+            key = ",".join([str(i) for i in config])
+
+            # 1. Not legal, just return `InvalidConfig`
+            if not is_legal:
+                result = dict(zip(self.searchspace.tune_params.keys(), config))
+                result[self.objective] = util.InvalidConfig()
+                final_results.append(result)
+
+            # 2. Attempt to retrieve from `unique_results` 
+            elif key in self.unique_results:
+                result = dict(self.unique_results[key])
+                legal_indices.append(index)
+                final_results.append(result)
+
+            # 3. We have already seen this config in the current batch
+            elif key in pending_indices_by_key:
+                pending_indices_by_key[key].append(index)
+                final_results.append(None)
+
+            # 4. A new config, we must evaluate this
+            else:
+                batch_keys.append(key)
+                batch_configs.append(config)
+                pending_indices_by_key[key] = [index]
+                final_results.append(None)
 
-        # snap values in x to nearest actual value for each parameter, unscale x if needed
-        if self.snap:
-            if self.scaling:
-                params = unscale_and_snap_to_nearest(x, self.searchspace.tune_params, self.tuning_options.eps)
+        # compile and benchmark the batch
+        batch_results = self.runner.run(batch_configs, self.tuning_options)
+
+        for key, result in zip(batch_keys, batch_results):
+            # Skip. Result is missing because the runner has exhausted the budget
+            if result is None:
+                continue
+
+            # set in the results array
+            for index in pending_indices_by_key[key]:
+                legal_indices.append(index)
+                final_results[index] = dict(result)
+
+                # Disable the timings. Only the first result must get these.
+                result["compile_time"] = 0
+                result["verification_time"] = 0
+                result["benchmark_time"] = 0
+
+            # Put result in `unique_results`
+            self.unique_results[key] = result
+
+        # Only things in `legal_indices` are valid results
+        for index in sorted(legal_indices):
+            self.results.append(final_results[index])
+
+        # upon returning from this function control will be given back to the strategy, so reset the start time
+        self.runner.last_strategy_start_time = perf_counter()
+
+        # this check is necessary because some strategies cannot handle partially completed requests
+        # for example when only half of the configs in a population have been evaluated
+        self.tuning_options.budget.raise_exception_if_done()
+        self.budget_spent_fraction = self.tuning_options.budget.get_fraction_consumed()
+
+        # If some results are missing (`None`), then the runner did not return all results 
+        # because the budget has been exceed or some other reason causing the runner to fail.
+        if not all(final_results):
+            raise util.StopCriterionReached("runner did not evaluate all given configurations")
+
+        return final_results
+
+    def eval_all(self, xs, check_restrictions=True):
+        """Cost function used by almost all strategies."""
+        results = self._run_configs(xs, check_restrictions=check_restrictions)
+        return_values = []
+
+        for result in results:
+            # get numerical return value, taking optimization direction into account
+            return_value = result[self.objective]
+
+            if not isinstance(return_value, util.ErrorConfig):
+                # this is a valid configuration, so invert value in case of maximization
+                if self.objective_higher_is_better:
+                    return_value = -return_value
             else:
-                params = snap_to_nearest_config(x, self.searchspace.tune_params)
-        else:
-            params = x
-        logging.debug("params %s", str(params))
+                # this is not a valid configuration, replace with float max if needed
+                if not self.return_invalid:
+                    return_value = sys.float_info.max
 
-        legal = True
-        result = {}
-        x_int = ",".join([str(i) for i in params])
+            # include raw data in return if requested
+            return_values.append(return_value)
 
-        # else check if this is a legal (non-restricted) configuration
-        if check_restrictions and self.searchspace.restrictions:
-            legal = self.searchspace.is_param_config_valid(tuple(params))
-
-
-            if not legal:
-                if "constraint_aware" in self.tuning_options.strategy_options and self.tuning_options.strategy_options["constraint_aware"]:
-                    # attempt to repair
-                    new_params = unscale_and_snap_to_nearest_valid(x, params, self.searchspace, self.tuning_options.eps)
-                    if new_params:
-                        params = new_params
-                        legal = True
-                        x_int = ",".join([str(i) for i in params])
-
-                if not legal:
-                    params_dict = dict(zip(self.searchspace.tune_params.keys(), params))
-                    result = params_dict
-                    result[self.tuning_options.objective] = util.InvalidConfig()
-
-        if legal:
-            # compile and benchmark this instance
-            res = self.runner.run([params], self.tuning_options)
-            result = res[0]
-
-            # append to tuning results
-            if x_int not in self.tuning_options.unique_results:
-                self.tuning_options.unique_results[x_int] = result
-
-            self.results.append(result)
-
-            # upon returning from this function control will be given back to the strategy, so reset the start time
-            self.runner.last_strategy_start_time = perf_counter()
-
-        # get numerical return value, taking optimization direction into account
-        return_value = result[self.tuning_options.objective]
-        if not isinstance(return_value, util.ErrorConfig):
-            # this is a valid configuration, so invert value in case of maximization
-            return_value = -return_value if self.tuning_options.objective_higher_is_better else return_value
-        else:
-            # this is not a valid configuration, replace with float max if needed
-            if not self.return_invalid:
-                return_value = self.invalid_return_value
+        return return_values
 
-        # include raw data in return if requested
-        if self.return_raw is not None:
-            try:
-                return return_value, result[self.return_raw]
-            except KeyError:
-                return return_value, [np.nan]
+    def eval(self, x, check_restrictions=True):
+        return self.eval_all([x], check_restrictions=check_restrictions)[0]
 
-        return return_value
+    def __call__(self, x, check_restrictions=True):
+        return self.eval(x, check_restrictions=check_restrictions)
+    
+    def get_results(self):
+        return self.results
+    
+    def get_num_unique_results(self):
+        return len(self.unique_results)
 
     def get_start_pos(self):
         """Get starting position for optimization."""
@@ -234,6 +306,11 @@ def get_bounds(self):
         return bounds
 
 
+def _get_nth_true(lst, n):
+    # Returns the index of the nth True value in a list
+    return [i for i, x in enumerate(lst) if x][n-1]
+
+
 def setup_method_arguments(method, bounds):
     """Prepare method specific arguments."""
     kwargs = {}
diff --git a/kernel_tuner/strategies/diff_evo.py b/kernel_tuner/strategies/diff_evo.py
index 29577581..ad1cd57c 100644
--- a/kernel_tuner/strategies/diff_evo.py
+++ b/kernel_tuner/strategies/diff_evo.py
@@ -115,7 +115,7 @@ def generate_population(tune_params, max_idx, popsize, searchspace, constraint_a
     return population
 
 
-def differential_evolution(searchspace, cost_func, bounds, popsize, maxiter, F, CR, method, constraint_aware, verbose):
+def differential_evolution(searchspace, cost_func: CostFunc, bounds, popsize, maxiter, F, CR, method, constraint_aware, verbose):
     """
     A basic implementation of the Differential Evolution algorithm.
 
@@ -139,7 +139,7 @@ def differential_evolution(searchspace, cost_func, bounds, popsize, maxiter, F,
     population[0] = cost_func.get_start_pos()
 
     # Calculate the initial cost for each individual in the population
-    population_cost = np.array([cost_func(ind) for ind in population])
+    population_cost = np.array(cost_func.eval_all(population))
 
     # Keep track of the best solution found so far
     best_idx = np.argmin(population_cost)
@@ -208,7 +208,7 @@ def differential_evolution(searchspace, cost_func, bounds, popsize, maxiter, F,
         # --- c. Selection ---
 
         # Calculate the cost of the new trial vectors
-        trial_population_cost = np.array([cost_func(ind) for ind in trial_population])
+        trial_population_cost = np.array(cost_func.eval_all(trial_population))
 
         # Keep track of whether population changes over time
         no_change = True
@@ -244,7 +244,7 @@ def differential_evolution(searchspace, cost_func, bounds, popsize, maxiter, F,
             print(f"Generation {generation + 1}, Best Cost: {best_cost:.6f}")
 
     if verbose:
-        print(f"Differential Evolution completed fevals={len(cost_func.tuning_options.unique_results)}")
+        print(f"Differential Evolution completed fevals={cost_func.get_num_unique_results()}")
 
     return {"solution": best_solution, "cost": best_cost}
 
diff --git a/kernel_tuner/strategies/firefly_algorithm.py b/kernel_tuner/strategies/firefly_algorithm.py
index a732d404..861c5f86 100644
--- a/kernel_tuner/strategies/firefly_algorithm.py
+++ b/kernel_tuner/strategies/firefly_algorithm.py
@@ -44,13 +44,14 @@ def tune(searchspace: Searchspace, runner, tuning_options):
     swarm[0].position = x0
 
     # compute initial intensities
-    for j in range(num_particles):
-        try:
+    try:
+        for j in range(num_particles):
             swarm[j].compute_intensity(cost_func)
-        except StopCriterionReached as e:
-            if tuning_options.verbose:
-                print(e)
-            return cost_func.results
+    except StopCriterionReached as e:
+        if tuning_options.verbose:
+            print(e)
+        return cost_func.results
+    for j in range(num_particles):
         if swarm[j].score <= best_score_global:
             best_position_global = swarm[j].position
             best_score_global = swarm[j].score
diff --git a/kernel_tuner/strategies/genetic_algorithm.py b/kernel_tuner/strategies/genetic_algorithm.py
index 804758ee..fa1f6cc9 100644
--- a/kernel_tuner/strategies/genetic_algorithm.py
+++ b/kernel_tuner/strategies/genetic_algorithm.py
@@ -43,19 +43,17 @@ def tune(searchspace: Searchspace, runner, tuning_options):
 
         # determine fitness of population members
         weighted_population = []
-        for dna in population:
-            try:
-                # if we are not constraint-aware we should check restrictions upon evaluation
-                time = cost_func(dna, check_restrictions=not constraint_aware)
-                num_evaluated += 1
-            except StopCriterionReached as e:
-                if tuning_options.verbose:
-                    print(e)
-                return cost_func.results
-
-            weighted_population.append((dna, time))
+        try:
+            # if we are not constraint-aware we should check restrictions upon evaluation
+            times = cost_func.eval_all(population, check_restrictions=not constraint_aware)
+            num_evaluated += len(population)
+        except StopCriterionReached as e:
+            if tuning_options.verbose:
+                print(e)
+            return cost_func.results
 
         # population is sorted such that better configs have higher chance of reproducing
+        weighted_population = list(zip(population, times))
         weighted_population.sort(key=lambda x: x[1])
 
         # 'best_score' is used only for printing
diff --git a/kernel_tuner/strategies/greedy_ils.py b/kernel_tuner/strategies/greedy_ils.py
index d9cf67ec..fde8bb13 100644
--- a/kernel_tuner/strategies/greedy_ils.py
+++ b/kernel_tuner/strategies/greedy_ils.py
@@ -37,16 +37,15 @@ def tune(searchspace: Searchspace, runner, tuning_options):
 
     last_improvement = 0
     while fevals < max_fevals:
-
         try:
-            candidate = base_hillclimb(candidate, neighbor, max_fevals, searchspace, tuning_options, cost_func, restart=restart, randomize=True)
+            candidate = base_hillclimb(candidate, neighbor, max_fevals, searchspace, cost_func, restart=restart, randomize=True)
             new_score = cost_func(candidate, check_restrictions=False)
         except StopCriterionReached as e:
             if tuning_options.verbose:
                 print(e)
             return cost_func.results
 
-        fevals = len(tuning_options.unique_results)
+        fevals = cost_func.get_num_unique_results()
         if new_score < best_score:
             last_improvement = 0
         else:
diff --git a/kernel_tuner/strategies/greedy_mls.py b/kernel_tuner/strategies/greedy_mls.py
index 4edd2f0a..dd02ff44 100644
--- a/kernel_tuner/strategies/greedy_mls.py
+++ b/kernel_tuner/strategies/greedy_mls.py
@@ -29,15 +29,14 @@ def tune(searchspace: Searchspace, runner, tuning_options):
     #while searching
     while fevals < max_fevals:
         try:
-            base_hillclimb(candidate, neighbor, max_fevals, searchspace, tuning_options, cost_func, restart=restart, randomize=randomize, order=order)
+            base_hillclimb(candidate, neighbor, max_fevals, searchspace, cost_func, restart=restart, randomize=randomize, order=order)
         except StopCriterionReached as e:
             if tuning_options.verbose:
                 print(e)
             return cost_func.results
 
         candidate = searchspace.get_random_sample(1)[0]
-
-        fevals = len(tuning_options.unique_results)
+        fevals = cost_func.get_num_unique_results()
 
     return cost_func.results
 
diff --git a/kernel_tuner/strategies/hillclimbers.py b/kernel_tuner/strategies/hillclimbers.py
index 120bc926..a4632801 100644
--- a/kernel_tuner/strategies/hillclimbers.py
+++ b/kernel_tuner/strategies/hillclimbers.py
@@ -4,7 +4,7 @@
 from kernel_tuner.strategies.common import CostFunc
 
 
-def base_hillclimb(base_sol: tuple, neighbor_method: str, max_fevals: int, searchspace: Searchspace, tuning_options,
+def base_hillclimb(base_sol: tuple, neighbor_method: str, max_fevals: int, searchspace: Searchspace,
                    cost_func: CostFunc, restart=True, randomize=True, order=None):
     """Hillclimbing search until max_fevals is reached or no improvement is found.
 
@@ -25,10 +25,6 @@ def base_hillclimb(base_sol: tuple, neighbor_method: str, max_fevals: int, searc
     :params searchspace: The searchspace object.
     :type searchspace: Seachspace
 
-    :param tuning_options: A dictionary with all options regarding the tuning
-        process.
-    :type tuning_options: dict
-
     :param cost_func: An instance of `kernel_tuner.strategies.common.CostFunc`
     :type runner: kernel_tuner.strategies.common.CostFunc
 
@@ -72,33 +68,39 @@ def base_hillclimb(base_sol: tuple, neighbor_method: str, max_fevals: int, searc
         if randomize:
             random.shuffle(indices)
 
+        children = []
+
         # in each dimension see the possible values
         for index in indices:
             neighbors = searchspace.get_param_neighbors(tuple(child), index, neighbor_method, randomize)
 
             # for each value in this dimension
             for val in neighbors:
-                orig_val = child[index]
+                child = list(child)
                 child[index] = val
+                children.append(child)
 
+        if restart:
+            for child in children:
                 # get score for this position
                 score = cost_func(child)
 
-                # generalize this to other tuning objectives
                 if score < best_score:
                     best_score = score
                     base_sol = child[:]
                     found_improved = True
-                    if restart:
-                        break
-                else:
-                    child[index] = orig_val
+                    break
+        else:
+            # get score for all positions in parallel
+            scores = cost_func.eval_all(children, check_restrictions=False)
 
-                fevals = len(tuning_options.unique_results)
-                if fevals >= max_fevals:
-                    return base_sol
+            for child, score in zip(children, scores):
+                if score < best_score:
+                    best_score = score
+                    base_sol = child[:]
+                    found_improved = True
 
-            if found_improved and restart:
-                break
+        if found_improved and restart:
+            break
 
     return base_sol
diff --git a/kernel_tuner/strategies/pso.py b/kernel_tuner/strategies/pso.py
index e8489d12..eefbc866 100644
--- a/kernel_tuner/strategies/pso.py
+++ b/kernel_tuner/strategies/pso.py
@@ -51,24 +51,26 @@ def tune(searchspace: Searchspace, runner, tuning_options):
         if tuning_options.verbose:
             print("start iteration ", i, "best time global", best_score_global)
 
+        try:
+            scores = cost_func.eval_all([p.position for p in swarm])
+        except StopCriterionReached as e:
+            if tuning_options.verbose:
+                print(e)
+            return cost_func.results
+
         # evaluate particle positions
-        for j in range(num_particles):
-            try:
-                swarm[j].evaluate(cost_func)
-            except StopCriterionReached as e:
-                if tuning_options.verbose:
-                    print(e)
-                return cost_func.results
+        for p, score in zip(swarm, scores):
+            p.set_score(score)
 
             # update global best if needed
-            if swarm[j].score <= best_score_global:
-                best_position_global = swarm[j].position
-                best_score_global = swarm[j].score
+            if score <= best_score_global:
+                best_position_global = p.position
+                best_score_global = score
 
         # update particle velocities and positions
-        for j in range(0, num_particles):
-            swarm[j].update_velocity(best_position_global, w, c1, c2)
-            swarm[j].update_position(bounds)
+        for p in swarm:
+            p.update_velocity(best_position_global, w, c1, c2)
+            p.update_position(bounds)
 
     if tuning_options.verbose:
         print("Final result:")
@@ -92,7 +94,10 @@ def __init__(self, bounds):
         self.score = sys.float_info.max
 
     def evaluate(self, cost_func):
-        self.score = cost_func(self.position)
+        self.set_score(cost_func(self.position))
+
+    def set_score(self, score):
+        self.score = score
         # update best_pos if needed
         if self.score < self.best_score:
             self.best_pos = self.position
diff --git a/kernel_tuner/strategies/pyatf_strategies.py b/kernel_tuner/strategies/pyatf_strategies.py
index d0d67778..1b82391c 100644
--- a/kernel_tuner/strategies/pyatf_strategies.py
+++ b/kernel_tuner/strategies/pyatf_strategies.py
@@ -85,7 +85,7 @@ def tune(searchspace: Searchspace, runner, tuning_options):
 
     try:
         # optimization loop (KT-compatible re-implementation of `make_step` from TuningRun)
-        while len(tuning_options.unique_results) < searchspace.size:
+        while cost_func.get_num_unique_results() < searchspace.size:
 
             # get new coordinates
             if not coordinates_or_indices:
diff --git a/kernel_tuner/strategies/random_sample.py b/kernel_tuner/strategies/random_sample.py
index 33b5075d..19440149 100644
--- a/kernel_tuner/strategies/random_sample.py
+++ b/kernel_tuner/strategies/random_sample.py
@@ -20,16 +20,13 @@ def tune(searchspace: Searchspace, runner, tuning_options):
         num_samples = min(tuning_options.max_fevals, searchspace.size)
 
     samples = searchspace.get_random_sample(num_samples)
-
     cost_func = CostFunc(searchspace, tuning_options, runner)
 
-    for sample in samples:
-        try:
-            cost_func(sample, check_restrictions=False)
-        except StopCriterionReached as e:
-            if tuning_options.verbose:
-                print(e)
-            return cost_func.results
+    try:
+        cost_func.eval_all(samples, check_restrictions=False)
+    except StopCriterionReached as e:
+        if tuning_options.verbose:
+            print(e)
 
     return cost_func.results
 
diff --git a/kernel_tuner/strategies/simulated_annealing.py b/kernel_tuner/strategies/simulated_annealing.py
index 962a1e34..b73bf0d6 100644
--- a/kernel_tuner/strategies/simulated_annealing.py
+++ b/kernel_tuner/strategies/simulated_annealing.py
@@ -68,7 +68,7 @@ def tune(searchspace: Searchspace, runner, tuning_options):
                 pos = new_pos
                 old_cost = new_cost
 
-        c = len(tuning_options.unique_results)
+        c = cost_func.get_num_unique_results()
         T = T_start * alpha**(max_iter/max_fevals*c)
 
         # check if solver gets stuck and if so restart from random position
diff --git a/kernel_tuner/util.py b/kernel_tuner/util.py
index 2c50bd6c..99c2a2f6 100644
--- a/kernel_tuner/util.py
+++ b/kernel_tuner/util.py
@@ -1,5 +1,6 @@
 """Module for kernel tuner utility functions."""
 import ast
+from datetime import timedelta
 import errno
 import json
 import logging
@@ -187,28 +188,75 @@ def check_argument_list(kernel_name, kernel_string, args):
         warnings.warn(errors[0], UserWarning)
 
 
-def check_stop_criterion(to: dict) -> float:
-    """Check if the stop criterion is reached.
+class TuningBudget:
+    def __init__(self, time_limit=None, max_fevals=None):
+        if time_limit is not None and not isinstance(time_limit, timedelta):
+            time_limit = timedelta(seconds=time_limit)
 
-    Args:
-        to (dict): tuning options.
+        if max_fevals is not None and max_fevals <= 0:
+            raise ValueError("max_fevals must be greater than zero")
+        
+        if time_limit is not None and time_limit <= timedelta(seconds=0):
+            raise ValueError("time_limit must be greater than zero")
 
-    Raises:
-        StopCriterionReached: if the max_fevals is reached or time limit is exceeded.
+        self.start_time_seconds = time.perf_counter()
+        self.time_spent_extra = timedelta()
+        self.time_limit = time_limit
+        self.num_fevals = 0
+        self.max_fevals = max_fevals
+    
+    def add_evaluations(self, n=1):
+        self.num_fevals += n
+    
+    def add_time(self, seconds=0, milliseconds=0):
+        self.time_spent_extra += timedelta(seconds=seconds, milliseconds=milliseconds)
+    
+    def get_time_spent(self) -> timedelta:
+        seconds_passed = time.perf_counter() - self.start_time_seconds
+        return timedelta(seconds=seconds_passed) + self.time_spent_extra
+    
+    def get_time_remaining(self) -> timedelta:
+        if self.time_limit is not None:
+            return max(self.time_limit - self.get_time_spent(), timedelta(seconds=0))
+        else:
+            return timedelta.max
+    
+    def get_evaluations_spent(self) -> int:
+        return self.num_fevals
+    
+    def get_evaluations_remaining(self) -> int:
+        if self.max_fevals is not None:
+            return max(self.max_fevals - self.num_fevals, 0)
+        else:
+            return float("inf")
+    
+    def is_done(self) -> bool:
+        if self.max_fevals is not None and self.num_fevals >= self.max_fevals:
+            return True
 
-    Returns:
-        float: fraction of budget spent. If both max_fevals and time_limit are set, it returns the fraction of time.
-    """
-    if "max_fevals" in to:
-        if len(to.unique_results) >= to.max_fevals:
-            raise StopCriterionReached(f"max_fevals ({to.max_fevals}) reached")
-        if not "time_limit" in to:
-            return len(to.unique_results) / to.max_fevals
-    if "time_limit" in to:
-        time_spent = (time.perf_counter() - to.start_time) + (to.simulated_time * 1e-3) + to.startup_time
-        if time_spent > to.time_limit:
+        if self.time_limit is not None and self.get_time_spent() > self.time_limit:
+            return True
+
+        return False   
+    
+    def raise_exception_if_done(self):
+        if self.max_fevals is not None and self.num_fevals >= self.max_fevals:
+            raise StopCriterionReached(f"max_fevals ({self.max_fevals}) reached")
+        
+        if self.time_limit is not None and self.get_time_spent() > self.time_limit:
             raise StopCriterionReached("time limit exceeded")
-        return time_spent / to.time_limit
+    
+    def get_fraction_consumed(self) -> float:
+        if self.max_fevals is not None and self.time_limit is not None:
+            time_spent = self.get_time_spent()
+            return min(1.0, time_spent / self.time_limit, self.num_fevals / self.max_fevals)
+        elif self.max_fevals is not None:
+            return min(1.0, self.num_fevals / self.max_fevals)
+        elif self.time_limit is not None:
+            return min(1.0, self.get_time_spent() / self.time_limit)
+        else:
+            return 0.0
+
     
 
 
@@ -684,17 +732,18 @@ def process_metrics(params, metrics):
     :rtype: dict
 
     """
-    if not isinstance(metrics, dict):
-        raise ValueError("metrics should be a dictionary to preserve order and support composability")
-    for k, v in metrics.items():
-        if isinstance(v, str):
-            value = eval(replace_param_occurrences(v, params))
-        elif callable(v):
-            value = v(params)
-        else:
-            raise ValueError("metric dicts values should be strings or callable")
-        # We overwrite any existing values for the given key
-        params[k] = value
+    if metrics is not None:
+        if not isinstance(metrics, dict):
+            raise ValueError("metrics should be a dictionary to preserve order and support composability")
+        for k, v in metrics.items():
+            if isinstance(v, str):
+                value = eval(replace_param_occurrences(v, params))
+            elif callable(v):
+                value = v(params)
+            else:
+                raise ValueError("metric dicts values should be strings or callable")
+            # We overwrite any existing values for the given key
+            params[k] = value
     return params
 
 
@@ -1140,7 +1189,7 @@ def check_matching_problem_size(cached_problem_size, problem_size):
     if cached_problem_size_arr.size != problem_size_arr.size or not (cached_problem_size_arr == problem_size_arr).all():
         raise ValueError(f"Cannot load cache which contains results for different problem_size, cache: {cached_problem_size}, requested: {problem_size}")
 
-def process_cache(cache, kernel_options, tuning_options, runner):
+def process_cache(cachefile, kernel_options, tuning_options, runner):
     """Cache file for storing tuned configurations.
 
     the cache file is stored using JSON and uses the following format:
@@ -1169,9 +1218,9 @@ def process_cache(cache, kernel_options, tuning_options, runner):
         raise ValueError("Caching only works correctly when tunable parameters are stored in a dictionary")
 
     # if file does not exist, create new cache
-    if not os.path.isfile(cache):
+    if not os.path.isfile(cachefile):
         if tuning_options.simulation_mode:
-            raise ValueError(f"Simulation mode requires an existing cachefile: file {cache} does not exist")
+            raise ValueError(f"Simulation mode requires an existing cachefile: file {cachefile} does not exist")
 
         c = dict()
         c["device_name"] = runner.dev.name
@@ -1185,15 +1234,14 @@ def process_cache(cache, kernel_options, tuning_options, runner):
         contents = json.dumps(c, cls=NpEncoder, indent="")[:-3]  # except the last "}\n}"
 
         # write the header to the cachefile
-        with open(cache, "w") as cachefile:
-            cachefile.write(contents)
+        with open(cachefile, "w") as f:
+            f.write(contents)
 
-        tuning_options.cachefile = cache
-        tuning_options.cache = {}
+        return {}
 
     # if file exists
     else:
-        cached_data = read_cache(cache, open_cache=not tuning_options.simulation_mode)
+        cached_data = read_cache(cachefile, open_cache=not tuning_options.simulation_mode)
 
         # if in simulation mode, use the device name from the cache file as the runner device name
         if runner.simulation_mode:
@@ -1219,17 +1267,16 @@ def process_cache(cache, kernel_options, tuning_options, runner):
                 )
             raise ValueError(
                 f"Cannot load cache which contains results obtained with different tunable parameters. \
-                Cache at '{cache}' has: {cached_data['tune_params_keys']}, tuning_options has: {list(tuning_options.tune_params.keys())}"
+                Cache at '{cachefile}' has: {cached_data['tune_params_keys']}, tuning_options has: {list(tuning_options.tune_params.keys())}"
             )
 
-        tuning_options.cachefile = cache
-        tuning_options.cache = cached_data["cache"]
+        return cached_data["cache"]
 
 
-def correct_open_cache(cache, open_cache=True):
+def correct_open_cache(cachefile, open_cache=True):
     """If cache file was not properly closed, pretend it was properly closed."""
-    with open(cache, "r") as cachefile:
-        filestr = cachefile.read().strip()
+    with open(cachefile, "r") as f:
+        filestr = f.read().strip()
 
     # if file was not properly closed, pretend it was properly closed
     if len(filestr) > 0 and filestr[-3:] not in ["}\n}", "}}}"]:
@@ -1241,15 +1288,15 @@ def correct_open_cache(cache, open_cache=True):
     else:
         if open_cache:
             # if it was properly closed, open it for appending new entries
-            with open(cache, "w") as cachefile:
-                cachefile.write(filestr[:-3] + ",")
+            with open(cachefile, "w") as f:
+                f.write(filestr[:-3] + ",")
 
     return filestr
 
 
-def read_cache(cache, open_cache=True):
+def read_cache(cachefile, open_cache=True):
     """Read the cachefile into a dictionary, if open_cache=True prepare the cachefile for appending."""
-    filestr = correct_open_cache(cache, open_cache)
+    filestr = correct_open_cache(cachefile, open_cache)
 
     error_configs = {
         "InvalidConfig": InvalidConfig(),
@@ -1267,25 +1314,25 @@ def read_cache(cache, open_cache=True):
     return cache_data
 
 
-def close_cache(cache):
-    if not os.path.isfile(cache):
+def close_cache(cachefile):
+    if not os.path.isfile(cachefile):
         raise ValueError("close_cache expects cache file to exist")
 
-    with open(cache, "r") as fh:
+    with open(cachefile, "r") as fh:
         contents = fh.read()
 
     # close to file to make sure it can be read by JSON parsers
     if contents[-1] == ",":
-        with open(cache, "w") as fh:
+        with open(cachefile, "w") as fh:
             fh.write(contents[:-1] + "}\n}")
 
 
-def store_cache(key, params, tuning_options):
+def store_cache(key, params, cachefile, cache):
     """Stores a new entry (key, params) to the cachefile."""
     # logging.debug('store_cache called, cache=%s, cachefile=%s' % (tuning_options.cache, tuning_options.cachefile))
-    if isinstance(tuning_options.cache, dict):
-        if key not in tuning_options.cache:
-            tuning_options.cache[key] = params
+    if isinstance(cache, dict):
+        if key not in cache:
+            cache[key] = params
 
             # Convert ErrorConfig objects to string, wanted to do this inside the JSONconverter but couldn't get it to work
             output_params = params.copy()
@@ -1293,9 +1340,9 @@ def store_cache(key, params, tuning_options):
                 if isinstance(v, ErrorConfig):
                     output_params[k] = str(v)
 
-            if tuning_options.cachefile:
-                with open(tuning_options.cachefile, "a") as cachefile:
-                    cachefile.write("\n" + json.dumps({key: output_params}, cls=NpEncoder)[1:-1] + ",")
+            if cachefile:
+                with open(cachefile, "a") as f:
+                    f.write("\n" + json.dumps({key: output_params}, cls=NpEncoder)[1:-1] + ",")
 
 
 def dump_cache(obj: str, tuning_options):
diff --git a/test/strategies/test_bayesian_optimization.py b/test/strategies/test_bayesian_optimization.py
index f8c889aa..5c7bab32 100644
--- a/test/strategies/test_bayesian_optimization.py
+++ b/test/strategies/test_bayesian_optimization.py
@@ -19,6 +19,8 @@
 strategy_options = dict(popsize=0, max_fevals=10)
 tuning_options = Options(dict(restrictions=[], tune_params=tune_params, strategy_options=strategy_options))
 tuning_options["scaling"] = True
+tuning_options["objective"] = "time"
+tuning_options["objective_higher_is_better"] = False
 tuning_options["snap"] = True
 max_threads = 1024
 searchspace = Searchspace(tune_params, [], max_threads)
diff --git a/test/strategies/test_common.py b/test/strategies/test_common.py
index 90f6c63e..54ecde6f 100644
--- a/test/strategies/test_common.py
+++ b/test/strategies/test_common.py
@@ -7,7 +7,7 @@
 from kernel_tuner.searchspace import Searchspace
 from kernel_tuner.strategies import common
 from kernel_tuner.strategies.common import CostFunc
-from kernel_tuner.util import StopCriterionReached
+from kernel_tuner.util import StopCriterionReached, TuningBudget
 
 try:
     from mock import Mock
@@ -30,7 +30,7 @@ def fake_runner():
 
 def test_cost_func():
     x = [1, 4]
-    tuning_options = Options(scaling=False, snap=False, tune_params=tune_params,
+    tuning_options = Options(tune_params=tune_params, budget=TuningBudget(),
                              restrictions=None, strategy_options={}, cache={}, unique_results={},
                              objective="time", objective_higher_is_better=False, metrics=None)
     runner = fake_runner()
@@ -41,14 +41,13 @@ def test_cost_func():
     # check if restrictions are properly handled
     def restrictions(x, y):
         return False
-    tuning_options = Options(scaling=False, snap=False, tune_params=tune_params,
+    tuning_options = Options(tune_params=tune_params, budget=TuningBudget(),
                              restrictions=restrictions, strategy_options={},
                              verbose=True, cache={}, unique_results={},
                              objective="time", objective_higher_is_better=False, metrics=None)
     
-    with raises(StopCriterionReached):
-        time = CostFunc(Searchspace(tune_params, restrictions, 1024), tuning_options, runner)(x)
-        assert time == sys.float_info.max
+    time = CostFunc(Searchspace(tune_params, restrictions, 1024), tuning_options, runner)(x)
+    assert time == sys.float_info.max
 
 
 def test_setup_method_arguments():
diff --git a/test/strategies/test_strategies.py b/test/strategies/test_strategies.py
index ea5a2994..63d01dbd 100644
--- a/test/strategies/test_strategies.py
+++ b/test/strategies/test_strategies.py
@@ -53,6 +53,7 @@ def vector_add():
         strategies.append(pytest.param(s, marks=skip_if_no_pyatf))
     else:
         strategies.append(s)
+
 @pytest.mark.parametrize('strategy', strategies)
 def test_strategies(vector_add, strategy):
     options = dict(popsize=5, neighbor='adjacent')
@@ -96,7 +97,7 @@ def test_strategies(vector_add, strategy):
         tune_params = vector_add[-1]
         unique_results = {}
         for result in results:
-            x_int = ",".join([str(v) for k, v in result.items() if k in tune_params])
+            x_int = ",".join([str(v) for k, v in result.items() if k in tune_params.keys()])
             if not isinstance(result["time"], InvalidConfig):
                 unique_results[x_int] = result["time"]
         assert len(unique_results) <= filter_options["max_fevals"]
diff --git a/test/test_common.py b/test/test_common.py
index 7c1bd683..e23a5588 100644
--- a/test/test_common.py
+++ b/test/test_common.py
@@ -1,20 +1,28 @@
 import random
 
 import numpy as np
+import pytest
 
 import kernel_tuner.strategies.common as common
+import kernel_tuner.util
 from kernel_tuner.interface import Options
 from kernel_tuner.searchspace import Searchspace
 
+@pytest.fixture
+def tuning_options():
+    tuning_options = Options()
+    tuning_options["strategy_options"] = {}
+    tuning_options["objective"] = "time"
+    tuning_options["objective_higher_is_better"] = False
+    tuning_options["budget"] = kernel_tuner.util.TuningBudget()
+    return tuning_options
+
 
-def test_get_bounds_x0_eps():
+def test_get_bounds_x0_eps(tuning_options):
     tune_params = dict()
     tune_params['x'] = [0, 1, 2, 3, 4]
     searchspace = Searchspace(tune_params, [], 1024)
 
-    tuning_options = Options()
-    tuning_options["strategy_options"] = {}
-
     bounds, x0, eps = common.CostFunc(searchspace, tuning_options, None, scaling=True).get_bounds_x0_eps()
 
     assert bounds == [(0.0, 1.0)]
@@ -27,7 +35,7 @@ def test_get_bounds_x0_eps():
     assert eps == 1.0
 
 
-def test_get_bounds():
+def test_get_bounds(tuning_options):
 
     tune_params = dict()
     tune_params['x'] = [0, 1, 2, 3, 4]
@@ -39,7 +47,7 @@ def test_get_bounds():
 
     expected = [(0, 4), (0, 9900), (-11.2, 123.27)]
     searchspace = Searchspace(tune_params, None, 1024)
-    cost_func = common.CostFunc(searchspace, None, None)
+    cost_func = common.CostFunc(searchspace, tuning_options, None)
     answer = cost_func.get_bounds()
     assert answer == expected
 
diff --git a/test/test_runners.py b/test/test_runners.py
index 3a0a26e2..609ccad3 100644
--- a/test/test_runners.py
+++ b/test/test_runners.py
@@ -163,8 +163,8 @@ def test_time_keeping(env):
     answer = [args[1] + args[2], None, None, None]
 
     options = dict(method="uniform",
-                   popsize=10,
-                   maxiter=1,
+                   popsize=5,
+                   maxiter=50,
                    mutation_chance=1,
                    max_fevals=10)
     start = time.perf_counter()
@@ -287,6 +287,7 @@ def test_runner(env):
     device_options = Options([(k, opts.get(k, None))
                               for k in _device_options.keys()])
     tuning_options.cachefile = None
+    tuning_options.unique_results = {}
 
     # create runner
     runner = SequentialRunner(kernelsource,
diff --git a/test/test_util_functions.py b/test/test_util_functions.py
index e785f415..4b63e864 100644
--- a/test/test_util_functions.py
+++ b/test/test_util_functions.py
@@ -3,6 +3,7 @@
 import json
 import os
 import warnings
+import datetime
 
 import numpy as np
 import pytest
@@ -429,6 +430,92 @@ def test_check_argument_list7():
     assert_user_warning(check_argument_list, [kernel_name, kernel_string, args])
 
 
+def test_tuning_budget1():
+    budget = TuningBudget()
+    assert budget.get_evaluations_spent() == 0
+    assert budget.get_evaluations_remaining() == float("inf")
+    assert not budget.is_done()
+    budget.raise_exception_if_done() # Should not raise
+    assert budget.get_fraction_consumed() == 0.0
+
+    budget.add_evaluations(9000)
+    assert budget.get_evaluations_spent() == 9000
+    assert budget.get_evaluations_remaining() == float("inf")
+    assert not budget.is_done()
+    budget.raise_exception_if_done() # Should not raise
+    assert budget.get_fraction_consumed() == 0.0
+
+    budget.add_time(seconds=9000)
+    assert budget.get_evaluations_spent() == 9000
+    assert budget.get_evaluations_remaining() == float("inf")
+    assert not budget.is_done()
+    budget.raise_exception_if_done() # Should not raise
+    assert budget.get_fraction_consumed() == 0.0
+
+def test_tuning_budget2():
+    budget = TuningBudget(max_fevals=5)
+    assert budget.get_evaluations_spent() == 0
+    assert budget.get_evaluations_remaining() == 5
+    assert not budget.is_done()
+    budget.raise_exception_if_done() # Should not raise
+    assert budget.get_fraction_consumed() == 0.0
+
+    budget.add_evaluations(4)
+    assert budget.get_evaluations_spent() == 4
+    assert budget.get_evaluations_remaining() == 1
+    assert not budget.is_done()
+    budget.raise_exception_if_done() # Should not raise
+    assert budget.get_fraction_consumed() == 4/5
+
+    budget.add_evaluations(1)
+    assert budget.get_evaluations_spent() == 5
+    assert budget.get_evaluations_remaining() == 0
+    assert budget.is_done()
+    assert pytest.raises(StopCriterionReached, budget.raise_exception_if_done)
+    assert budget.get_fraction_consumed() == 1.0
+
+
+def test_tuning_budget3():
+    # Two values are similar if they are within 0.01
+    approx = lambda x: pytest.approx(x, abs=0.01)
+
+    budget = TuningBudget(time_limit=5)
+    assert budget.get_time_spent().total_seconds() == approx(0)
+    assert budget.get_time_remaining().total_seconds() == approx(5)
+    assert budget.get_evaluations_spent() == 0
+    assert budget.get_evaluations_remaining() == float("inf")
+    assert not budget.is_done()
+    budget.raise_exception_if_done() # Should not raise
+    assert budget.get_fraction_consumed() == approx(0.0)
+
+    budget.add_evaluations(1)
+    assert budget.get_time_spent().total_seconds() == approx(0)
+    assert budget.get_time_remaining().total_seconds() == approx(5)
+    assert budget.get_evaluations_spent() == 1
+    assert budget.get_evaluations_remaining() == float("inf")
+    assert not budget.is_done()
+    budget.raise_exception_if_done() # Should not raise
+    assert budget.get_fraction_consumed() == approx(0.0)
+
+    budget.add_time(seconds=2)
+    assert budget.get_time_spent().total_seconds() == approx(2)
+    assert budget.get_time_remaining().total_seconds() == approx(3)
+    assert budget.get_evaluations_spent() == 1
+    assert budget.get_evaluations_remaining() == float("inf")
+    assert not budget.is_done()
+    budget.raise_exception_if_done() # Should not raise
+    assert budget.get_fraction_consumed() == approx(2/5)
+
+    budget.add_time(seconds=4)
+    assert budget.get_time_spent().total_seconds() == approx(6)
+    assert budget.get_time_remaining().total_seconds() == approx(0)
+    assert budget.get_evaluations_spent() == 1
+    assert budget.get_evaluations_remaining() == float("inf")
+    assert budget.is_done()
+    assert pytest.raises(StopCriterionReached, budget.raise_exception_if_done)
+    assert budget.get_fraction_consumed() == 1.0
+
+
 def test_check_tune_params_list():
     tune_params = dict(
         zip(
@@ -611,25 +698,25 @@ def assert_open_cachefile_is_correctly_parsed(cache):
 
     try:
         # call process_cache without pre-existing cache
-        process_cache(cache, kernel_options, tuning_options, runner)
+        tuning_options.cachefile = cache
+        tuning_options.cache = process_cache(cache, kernel_options, tuning_options, runner)
 
         # check if file has been created
         assert os.path.isfile(cache)
         assert_open_cachefile_is_correctly_parsed(cache)
-        assert tuning_options.cachefile == cache
         assert isinstance(tuning_options.cache, dict)
         assert len(tuning_options.cache) == 0
 
         # store one entry in the cache
         params = {"x": 4, "time": np.float32(0.1234)}
-        store_cache("4", params, tuning_options)
+        store_cache("4", params, cache, tuning_options.cache)
         assert len(tuning_options.cache) == 1
 
         # close the cache
         close_cache(cache)
 
         # now test process cache with a pre-existing cache file
-        process_cache(cache, kernel_options, tuning_options, runner)
+        tuning_options.cache = process_cache(cache, kernel_options, tuning_options, runner)
         assert_open_cachefile_is_correctly_parsed(cache)
 
         assert tuning_options.cache["4"]["time"] == params["time"]
@@ -638,7 +725,7 @@ def assert_open_cachefile_is_correctly_parsed(cache):
         # a different kernel, device, or parameter set
         with pytest.raises(ValueError) as excep:
             kernel_options.kernel_name = "wrong_kernel"
-            process_cache(cache, kernel_options, tuning_options, runner)
+            tuning_options.cache = process_cache(cache, kernel_options, tuning_options, runner)
         assert "kernel" in str(excep.value)
 
         # correct the kernel name from last test
@@ -646,7 +733,7 @@ def assert_open_cachefile_is_correctly_parsed(cache):
 
         with pytest.raises(ValueError) as excep:
             runner.dev.name = "wrong_device"
-            process_cache(cache, kernel_options, tuning_options, runner)
+            tuning_options.cache = process_cache(cache, kernel_options, tuning_options, runner)
         assert "device" in str(excep.value)
 
         # correct the device from last test
@@ -654,7 +741,7 @@ def assert_open_cachefile_is_correctly_parsed(cache):
 
         with pytest.raises(ValueError) as excep:
             tuning_options.tune_params["y"] = ["a", "b"]
-            process_cache(cache, kernel_options, tuning_options, runner)
+            tuning_options.cache = process_cache(cache, kernel_options, tuning_options, runner)
         assert "parameter" in str(excep.value)
 
     finally: