Multi segment cagra search#133
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jamxia155 wants to merge 5 commits intorapidsai:mainfrom
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Overrides rewrite() to run all segment searches into a shared device buffer and merge with cuvsSelectK entirely on GPU, eliminating per-segment D2H copies and CPU-side TopDocs.merge(). Falls back to the standard Lucene per-segment path when any segment lacks a CAGRA index, an explicit filter is set, or k > 1024. Also adds ordToDoc() and getCagraIndexForField() helpers to CuVS2510GPUVectorsReader to support result decoding. Fixes for Lucene 10.2 API changes: CodecReader moved to org.apache.lucene.index; createRewrittenQuery() removed and replaced with an inline docAndScoreQuery() implementation using the public Weight/ScorerSupplier API.
- CuVS2510GPUVectorsFormat: call CuVSProvider.provider().enableRMMAsyncMemory() in the static initializer so that cuda_async_memory_resource is active for the lifetime of the codec. This makes CAGRA workspace deallocations stream-ordered and non-blocking, which is required for the CudaStreamPool to provide any parallelism benefit. - GPUKnnFloatVectorQuery: upload the query vector to device once before the per-segment loop and share the resulting CuVSMatrix across all CagraQuery instances, reducing host-to-device copies from O(numSegments) to 1 per query. Wrap the shared device matrix in try-with-resources to close the RMM allocation promptly after MultiSegmentCagraSearch.search() returns. - FilterCuVSProvider: delegate enableRMMAsyncMemory() to the wrapped provider.
GPUKnnFloatVectorQuery / GPUPerLeafCuVSKnnCollector: - Add persistent, persistentLifetime, and persistentDeviceUsage parameters, threaded through all constructor overloads and forwarded to CagraSearchParams.Builder in both the multi-segment rewrite() path and the per-segment approximateSearch() fallback path. - Add threadBlockSize parameter (0 = auto) to allow tuning of the persistent kernel's worker_queue_size, which determines how many concurrent query threads can run without latency increase. Fix persistent-runner hash instability across segments (rewrite() path): - When max_iterations is 0 (auto), CAGRA computes it from each segment's dataset size. Different-sized segments produce different values, causing a distinct runner hash per segment and a destroy/recreate cycle on every search call. - Add computeMaxIterations(), which mirrors adjust_search_params() from search_plan.cuh, and call it once using the largest segment's graph size and degree. All segments then share the same max_iterations, producing a stable runner hash across the full multi-segment query. CuVS2510GPUVectorsReader: - Forward threadBlockSize, persistent, persistentLifetime, and persistentDeviceUsage from GPUPerLeafCuVSKnnCollector to CagraSearchParams.Builder in the per-segment fallback path.
Remove persistent kernel mode: - Drop persistent, persistentLifetime, and persistentDeviceUsage fields and parameters from GPUKnnFloatVectorQuery, GPUPerLeafCuVSKnnCollector, and CuVS2510GPUVectorsReader. The persistent kernel is superseded by the native multi-segment kernel (cuvsCagraSearchMultiSegment) which achieves better concurrency without the per-runner lifecycle overhead. - Collapse the 11-argument GPUKnnFloatVectorQuery constructor (which only existed to accept persistent parameters) into the standard 8-argument form. - Remove stale comments that described max_iterations uniformity in terms of persistent-runner hash stability; replace with accurate explanation (consistent search quality across segments of different sizes). Add workspace pool configuration: - Add WORKSPACE_POOL_SIZE_PROPERTY constant to ThreadLocalCuVSResourcesProvider. - On resources creation, read com.nvidia.cuvs.workspacePoolSize system property and call setWorkspacePool() if set, so callers can pre-warm the per-thread RMM pool without modifying cuvs-lucene source.
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Companion PR to cuvs!2035, addresses #124.
Existing CAGRA search code path for each search query:
Proposed change: