osobhandClaude Fable 5.1
eb99de1020
New clawhdf5-bench binary `search_harness`, the measurement baseline for the search hot-path work. On deterministic clustered 384-dim data it reports HNSW build time and, per ef, recall@10 against an exact scan, QPS and p50/p99; and for HDF5Memory: ingest, checkpoint, open, first-query-after-open and steady-state hybrid_search latency at 1K/10K (and 100K with --full). Optional JSON output for tracking. Baseline recorded in BENCHMARKS.md. It shows two problems: HNSW recall@10 does not respond to ef and falls from 0.87 (1K) to 0.31 (100K) on clustered data, and end-to-end hybrid_search is ~1000x slower than its vector stage because every query rebuilds BM25 and rewrites the .h5 file. Co-Authored-By: Claude Fable 5.1 <[email protected]>
86 lines
2.6 KiB
TOML
86 lines
2.6 KiB
TOML
[package]
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name = "clawhdf5-bench"
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version = "2.3.0"
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edition = "2024"
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description = "Benchmark harnesses for clawhdf5-agent (Track 8)"
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license = "MIT"
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[[bin]]
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name = "longmemeval_bench"
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path = "src/bin/longmemeval_bench.rs"
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[[bin]]
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name = "memory_arena"
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path = "src/bin/memory_arena.rs"
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[[bin]]
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name = "search_harness"
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path = "src/bin/search_harness.rs"
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[[bin]]
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name = "footprint_bench"
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path = "src/bin/footprint_bench.rs"
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[[bin]]
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name = "consolidation_efficiency"
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path = "src/bin/consolidation_efficiency.rs"
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[[bin]]
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name = "ephemeral_perf"
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path = "src/bin/ephemeral_perf.rs"
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[[bin]]
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name = "mpi_io_bench"
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path = "src/bin/mpi_io_bench.rs"
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required-features = ["mpi-io"]
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# ---------------------------------------------------------------------------
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# h5bench-equivalent Criterion benchmarks
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# ---------------------------------------------------------------------------
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[[bench]]
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name = "h5bench_write"
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harness = false
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[[bench]]
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name = "h5bench_read"
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harness = false
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[[bench]]
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name = "h5bench_meta"
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harness = false
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[dependencies]
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clawhdf5-agent = { path = "../clawhdf5-agent" }
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clawhdf5-ann = { path = "../clawhdf5-ann" }
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clawhdf5-io = { path = "../clawhdf5-io" }
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mpi = { version = "0.8", optional = true }
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serde = { workspace = true }
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serde_json = "1"
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tempfile = { workspace = true }
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# Optional: libhdf5 C wrapper for side-by-side comparison (requires system libhdf5).
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# Enable with: cargo bench -p clawhdf5-bench --features libhdf5-compare
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# Uses hdf5-metno (fork of hdf5 crate) which supports HDF5 1.14.x.
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hdf5 = { version = "0.12", optional = true, package = "hdf5-metno" }
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# Optional: real sentence embeddings for the LongMemEval bench's vector stage.
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# Enable with: cargo run --release --bin longmemeval_bench --features embeddings
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# Off by default — nothing in the shipped crates depends on these.
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candle-core = { version = "0.9", optional = true }
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candle-nn = { version = "0.9", optional = true }
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candle-transformers = { version = "0.9", optional = true }
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tokenizers = { version = "0.21", optional = true }
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[dev-dependencies]
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clawhdf5 = { path = "../clawhdf5", features = ["zstd", "pcodec"] }
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criterion = { workspace = true }
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[features]
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# When enabled, benchmarks add matching libhdf5 variants for side-by-side comparison.
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libhdf5-compare = ["hdf5"]
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mpi-io = ["clawhdf5-io/mpi-io", "mpi"]
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# Real MiniLM embeddings for longmemeval_bench, so the vector stage is not inert.
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embeddings = ["candle-core", "candle-nn", "candle-transformers", "tokenizers"]
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# CUDA-accelerated embedding. MiniLM on a CPU takes hours over the full
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# longmemeval_s haystack; on a GPU it is minutes.
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embeddings-cuda = ["embeddings", "candle-core/cuda", "candle-nn/cuda", "candle-transformers/cuda"]
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