首次用有限域代数学习精确的NVIDIA SASS编码器，支持最新显卡机器码重写。
Learning Exact NVIDIA SASS Encoders with $\mathbb{F}_2$ Linear Algebra
- 基于F2域线性代数，将SASS编码建模为向量仿射映射。
- 在3225个CUBIN上训练，往返测试中所有代码段完全复原无损。
- 开源支持SM107，可分离控制位与重定位规则，适合底层优化研究者。
NVIDIA提供SASS反汇编工具但未公开汇编器，限制了对数据中心GPU机器码的可控重写。本文提出F2Asm，通过成对的反汇编结果与原始CUBIN指令，学习精确的128位SASS编码器。据我们所知，F2Asm是首个将SASS指令编码建模为F2域上的向量值仿射映射的系统，也是首个支持Rubin SM107的开源NVIDIA SASS汇编器。F2Asm利用F2域上的高斯消元法，逐步构建紧凑基底，检测不一致并拒绝超出学习范围的输入。该方法将目标特定的控制位、重定位规则和CUBIN元数据与学习算法解耦。我们在3,225个来自指定NVIDIA及第三方生产库、CUDA 13.3与13.4开发者预览版存档的CUBIN上训练了针对Hopper SM90/SM90a、Blackwell SM100和Rubin SM107的编码器。在往返测试中，对每个CUBIN进行反汇编后再重新汇编，所有对比的可执行文本节均与原文件完全匹配。
原文摘要 · Abstract (English)
NVIDIA provides a SASS disassembler but no public SASS assembler for recent data-center GPUs, limiting controlled machine-code rewriting. We present F2Asm, which learns exact 128-bit SASS encoders from paired disassembly and original CUBIN instruction words. To our knowledge, F2Asm is the first system to learn SASS instruction encoders as vector-valued affine maps over F2 and the first open-source NVIDIA SASS assembler to support Rubin SM107. F2Asm uses Gaussian elimination over F2 to incrementally build a compact basis, detect inconsistencies, and reject inputs outside the learned span. F2Asm separates target-specific control bits, relocation rules, and CUBIN metadata from its learning algorithm. We train encoders for Hopper SM90/SM90a, Blackwell SM100, and Rubin SM107 using 3,225 CUBINs from pinned NVIDIA and third-party production libraries, CUDA 13.3 packages, and CUDA 13.4 Developer Preview archives. In round-trip tests, F2Asm reassembles the disassembled SASS for each CUBIN, and all compared executable text sections match the originals exactly.
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