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

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 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.

Machine Learning (cs.LG)

arXiv:2608.20532 [cs.LG]

(or arXiv:2608.20532v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.20532 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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