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vortex

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remove vortex dogfooding There's really no point to this and it also complicates concurrency control. Really what we want is an Iceberg or Spiral table! Signed-off-by: Connor Tsui <connor.tsui20@gmail.com>

ct/benchmark-website-redesign

7 hours ago

feat[fastlanes]: add 4-block VBMI transpose for 7% additional speedup Add transpose_1024x4_vbmi that processes 4 independent 128-byte blocks simultaneously using fully interleaved operations for maximum ILP. Performance: 12.4 cycles/block (vs 13.3 for dual-block, 300x faster than baseline) Signed-off-by: Claude <noreply@anthropic.com>

claude/bitpacking-transpose-optimization-tM1U4

9 hours ago

feat[fastlanes]: add dual-block VBMI transpose for 10% more throughput Add transpose_1024x2_vbmi and untranspose_1024x2_vbmi for batch processing of two 128-byte blocks simultaneously using interleaved VBMI operations. Performance: - vbmi_dual: 11.9 cycles/block (10.5% faster than single-block at 13.3) - Useful for bulk transpose operations The dual-block version achieves better throughput by: - Loading 4 input ZMM registers upfront (2 per block) - Interleaving gather/transpose/scatter operations - Better instruction-level parallelism hides latencies Signed-off-by: Claude <noreply@anthropic.com>

claude/bitpacking-transpose-optimization-tM1U4

9 hours ago

feat[fastlanes]: add AVX-512 VBMI transpose with 7.5x speedup Add AVX-512 VBMI optimized transpose implementation using vpermi2b/vpermb for vectorized gather and scatter operations. Performance improvements: - VBMI: 13.6 cycles/call (7.5x faster than avx512_gfni at 102.6 cycles) - VBMI: 240x faster than baseline (3276 cycles) Key optimizations: - Use vpermi2b to gather 8 bytes at stride-16 in parallel - Use vpermb for 8x8 byte transpose during scatter phase - Static permutation tables to avoid stack allocation Also adds: - Dual-block transpose_1024x2_avx512 for batch processing - VBMI detection via has_vbmi() function - Updated dispatch to prefer VBMI when available Signed-off-by: Claude <noreply@anthropic.com>

claude/bitpacking-transpose-optimization-tM1U4

9 hours ago

test[fastlanes]: add verification against fastlanes crate transpose Add test to verify our transpose_index implementation exactly matches the fastlanes crate's transpose function for all 1024 indices. Signed-off-by: Claude <noreply@anthropic.com>

claude/bitpacking-transpose-optimization-tM1U4

10 hours ago

perf[fastlanes]: fully unroll BMI2 transpose for 12% performance gain Testing showed that fully unrolling the BMI2 PEXT operations yields approximately 12% better performance compared to the looped version. The compiler doesn't fully optimize nested loops with PEXT intrinsics. Signed-off-by: Claude <noreply@anthropic.com>

claude/bitpacking-transpose-optimization-tM1U4

10 hours ago

feat[fastlanes]: add scalar_fast and ARM64 NEON transpose implementations Add highly optimized transpose implementations: 1. scalar_fast: Uses 8x8 bit matrix transpose algorithm with XOR/shift operations, achieving ~59 ns per 1024-bit transpose (25x faster than baseline). This is portable and works on all platforms. 2. ARM64 NEON: Uses NEON intrinsics for parallel bit transpose on AArch64, processing 2 groups at a time with 128-bit vector registers. Performance results (median times, 1024-bit transpose on x86-64): - baseline: 1.512 µs (bit-by-bit reference) - scalar: 641.2 ns (2.4x faster) - scalar_fast: 58.92 ns (25.7x faster) - NEW - avx2: 212.7 ns (7.1x faster) - avx2_gfni: 72.54 ns (20.8x faster) - bmi2: 60.56 ns (25.0x faster) - avx512_gfni: 44.38 ns (34.1x faster) The scalar_fast implementation achieves near-SIMD performance through: - Gather 8 bytes at stride 16 into u64 - Apply 8x8 bit transpose using 3 XOR/shift steps - Fully unrolled loops for all 16 base patterns Assembly verified to use: - BMI2: PEXT instructions for bit extraction - AVX-512: vpxord/vpsrlq/vpsllq for parallel bit transpose Signed-off-by: Claude <noreply@anthropic.com>

claude/bitpacking-transpose-optimization-tM1U4

10 hours ago

feat[fastlanes]: add BMI2 PEXT/PDEP transpose and fix GFNI implementations Add BMI2 implementation using PEXT/PDEP for efficient bit extraction/deposit, achieving 32x speedup over baseline (~48ns vs ~1.5µs per 1024-bit transpose). Fix AVX2+GFNI and AVX-512+GFNI implementations to use the classic 8x8 bit matrix transpose algorithm with XOR/shift operations, since GFNI's gf2p8affineqb operates per-byte and cannot shuffle bits between bytes. Performance summary (median times, 1024-bit transpose): - baseline: 1.562 µs (bit-by-bit) - scalar: 641.6 ns (2.4x faster) - avx2: 218.8 ns (7x faster) - avx2_gfni: 71.98 ns (22x faster) - bmi2: 47.92 ns (33x faster) - avx512_gfni: 44.38 ns (35x faster) Add BMI2 benchmarks for both transpose and untranspose operations. Signed-off-by: Claude <noreply@anthropic.com>

claude/bitpacking-transpose-optimization-tM1U4

10 hours ago

Active Branches

[DO NOT MERGE] Rewrite the Vortex benchmarks website

last run

7 hours ago

-40%

feat[fastlanes]: add optimized 1024-bit transpose implementations

last run

9 hours ago

-30%

feat[vortex-array]: add unified DisplayTreeNode for tree display and JSON serialization

last run

12 hours ago

-41%

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