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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 57 |
1 files changed, 27 insertions, 30 deletions
@@ -94,36 +94,33 @@ int main(void) { ## Benchmarks -1 000 000 u64→u64 entries: insert, lookup (hit), lookup (miss), remove. -All implementations use the same murmur-inspired mixing hash where the language allows it. -Best time across multiple runs is reported — see notes on V-Cache below. - -**System:** AMD Ryzen 9 7950X3D, Clang 22.1.1 / GCC 15.2.1 / Rust 1.93.0, Linux, performance governor. - -``` -Implementation Best (ms) ---------------------------------------------- -abseil flat_hash_map (C++) 34 [1] -cheesemap SSE2 (Clang) 45 -Java HashMap ~32 [2] -Rust HashMap (hashbrown) 48 -cheesemap scalar (Clang) 56 -Go map 1.26 (Swiss table) 68 -.NET Dictionary 70 -Chromium JS Map 102 -std::unordered_map + mimalloc ~110 [3] -cheesemap SSE2 (GCC) 58 [4] -Node.js Map 153 -std::unordered_map (Clang) 176 -Python dict 193 -``` - -[1] abseil is the reference Swiss table implementation — its numbers are expected to be strong. -[2] Java's best run benefits from C2 JIT reaching peak tier during the measured phase; average is ~67 ms. -[3] std::unordered_map is a chained hash map; mimalloc recovers ~38% over the system allocator by reducing per-node allocation overhead. Without mimalloc the best observed time was ~176 ms. -[4] GCC's codegen for this workload is ~25% slower than Clang due to a failure to inline the hot lookup path. - -**Note on AMD 3D V-Cache:** The Ryzen 9 7950X3D has two CCDs — one with 96 MB L3 (3D V-Cache) and one with 32 MB L3. The benchmark's working set (~32 MB) sits right on the boundary of the smaller CCD. The OS scheduler assigns the process to a CCD at random, producing a bimodal result distribution: runs landing on the V-Cache CCD are significantly faster. Best times above reflect V-Cache-lucky runs. Average times are roughly 1.5–2× higher on this machine. To pin to the V-Cache CCD for reproducible results: `numactl --cpunodebind=0 ./bench`. +1,000,000 `u64 -> u64` entries: insert, lookup hit, lookup miss, remove. All implementations use the same murmur-inspired mixing hash where the language allows it. Best time across multiple runs is shown. + +**Platform:** x86-64, Linux, performance governor. + +| Implementation | Best (ms) | Notes | +|---|---|---| +| Java HashMap | ~32 | [1] | +| abseil flat_hash_map | ~34 | [2] | +| cheesemap SSE2 (Clang) | ~45 | | +| Rust HashMap (hashbrown) | ~48 | | +| cheesemap scalar (GCC) | ~53 | | +| cheesemap scalar (Clang) | ~56 | | +| cheesemap SSE2 (GCC) | ~58 | [3] | +| Go map (Swiss table) | ~68 | | +| .NET Dictionary | ~70 | | +| Chromium JS Map | ~102 | | +| std::unordered_map + mimalloc | ~110 | [4] | +| Node.js Map | ~153 | | +| std::unordered_map | ~176 | | +| Python dict | ~193 | | + +> **Note on extended L3 cache:** The test machine has a split cache topology where the benchmark's ~32 MB working set fits comfortably in one cache domain but thrashes in the other. Best times above reflect lucky scheduling onto the larger cache. Average times are roughly 1.5-2x higher. Pin with `numactl --cpunodebind=0` for reproducible results. + +**[1]** JVM C2 JIT reaching peak tier mid-run inflates the best; average is ~67 ms. +**[2]** abseil is the reference Swiss table implementation. +**[3]** GCC is ~25% slower than Clang here due to a failure to inline the hot lookup path. +**[4]** std::unordered_map is chained; mimalloc recovers ~38% by reducing per-node allocation cost. Without it: ~176 ms. --- |