Learning Exact NVIDIA SASS Encoders with $\mathbb{F}_2$ Linear Algebra

תקציר מקורי באנגליתarXiv:2608.20532v2 Announce Type: replace Abstract: 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 \(\mathbb{F}_2\) and the first open-source NVIDIA SASS assembler to support Rubin SM107. F2Asm uses Gaussian elimination over \(\mathbb{F}_2\) 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 enco