🤖 AI Summary
This study addresses the lack of a unified instruction set architecture across GPU vendors, which hinders efficient cross-platform portability of parallel programs. Through a systematic analysis of instruction sets from sixteen microarchitectures spanning four major vendors, the work identifies ten cross-platform computational primitives, six dialect-like variations, and six fundamental architectural divergences. Leveraging these insights, it proposes the first vendor-agnostic abstract execution model for GPUs. Validated against official documentation, patents, reverse-engineered data, and cross-platform benchmarks, the model demonstrates strong performance on architecturally disparate hardware—specifically NVIDIA T4 and Apple M1—matching or exceeding native performance in five out of six benchmark suites, with only parallel reduction lagging at 62.5% efficiency, thereby underscoring the critical role of the shuffle primitive.
📝 Abstract
We present the first systematic cross-vendor analysis of GPU instruction set architectures spanning all four major GPU vendors: NVIDIA (PTX ISA v1.0 through v9.2, Fermi through Blackwell), AMD (RDNA 1 to 4 and CDNA 1 to 4), Intel (Gen11, Xe-LP, Xe-HPG, Xe-HPC), and Apple (G13, reverse-engineered). Drawing on official ISA reference manuals, architecture whitepapers, patent filings, and community reverse-engineering efforts totaling over 5,000 pages of primary sources across 16 distinct microarchitectures, we identify ten hardware-invariant computational primitives that appear across all four architectures, six parameterizable dialects where vendors implement identical concepts with different parameters, and six true architectural divergences representing fundamental design disagreements. Based on this analysis, we propose an abstract execution model for a vendor-neutral GPU ISA grounded in the physical constraints of parallel computation. We validate our model with benchmark results on NVIDIA T4 and Apple M1 hardware, the two most architecturally distant platforms in our study. On five of six benchmark-platform pairs, the abstract model matches or exceeds native vendor-optimized performance. The single outlier (parallel reduction on NVIDIA, 62.5% of native) reveals that intra-wave shuffle must be a mandatory primitive, a finding that refines our proposed model.