gh-124951: Optimize base64 encode & decode for an easy 2-3x speedup [no SIMD] #143262
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Add Tools/binasciibench/binasciibench.py benchmark for measuring base64
encoding/decoding throughput.
Optimize base64 encoding/decoding by eliminating loop-carried dependencies.
Key changes:
- Add base64_encode_trio() and base64_decode_quad() helper functions
that process complete groups independently
- Add base64_encode_fast() and base64_decode_fast() wrappers
- Update b2a_base64 and a2b_base64 to use fast path for complete groups
Performance gains (encode/decode speedup vs main, PGO builds):
64 bytes 64K 1M
Zen2: 1.1x/1.6x 1.6x/2.4x 1.4x/2.4x
Zen4: 1.2x/1.7x 1.6x/3.0x 1.5x/3.0x
M4: 1.3x/1.9x 2.3x/2.8x 2.4x/2.9x
RPi5-32: 1.4x/1.4x 2.4x/2.0x 2.0x/1.9x
Additional SIMD implementations (NEON, AVX-512 VBMI) can achieve
+50% to +1500% further gains and are planned for follow-on work.
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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MSVC doesn't support forward declarations of arrays without explicit size. Move the table definition before the inline functions that use it, eliminating the need for a forward declaration. Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
picnixz
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Dec 29, 2025
Co-authored-by: Bénédikt Tran <10796600+picnixz@users.noreply.github.com>
Add Py_ALIGNED(64) to both lookup tables to ensure each fits within a single L1 cache line, reducing potential cache misses during encoding/decoding loops. Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Replace hardcoded '=' characters with the BASE64_PAD macro for consistency with the rest of the codebase. Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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Optimize base64 encoding/decoding by eliminating loop-carried dependencies. Key changes:
base64_encode_trio()andbase64_decode_quad()helper functions that process complete groups independentlybase64_encode_fast()andbase64_decode_fast()wrappersb2a_base64anda2b_base64to use fast path for complete groupsThe binasciibench I used measuring base64 encoding/decoding throughput is included in commit history, but i pulled it out of the PR in favor of adding to pyperformance.
Performance gains (encode/decode speedup vs main, PGO builds):
Additional SIMD implementations (NEON, AVX-512 VBMI) can achieve +50% (M4) to +1500% (!! Zen4) further gains and are planned for follow-on work if deemed simple to maintain.
Widely used third party libraries contain industry canonical SIMD accelerated variants such as simdutf (C++ based unfortunately) so the decision of how to link and use those and when is best kept separate.
This PR's simple pure better use of modern CPU functional unit pipelining wins make sense regardless.
Based on my exploratory work done in main...gpshead:cpython:claude/vectorize-base64-c-S7Hku
base64module: Link against SIMD library for 10x performance. #124951