← 研踪 PaperTrailarXiv:2610.37466 · v1
Kernel Performance Engineering under Physical Non-Idealities
首次提交 2026-10-28 00:00
版本更新 2026-10-28 00:00
雷达观测 2026-10-29 00:34
摘要
Physical non-idealities in integrated circuit (IC) design and fabrication are concealed by conventional hardware abstractions, yet they can limit kernel performance and obscure optimization opportunities. Using distributed shared memory (DSMEM) on NVIDIA H100 GPUs as a case study, we show how floorsweeping restricts hardware resources available for clustered execution, potentially offsetting the benefits of reduced data movement enabled by DSMEM. Guided by these insights, we develop a wave-aware clustering strategy that accounts for these resource constraints to improve kernel performance. Evaluations using a global-load-and-broadcast microbenchmark, GEMM, and the Hopper version of FlashAttention 4 demonstrate the performance implications of these constraints, with wave-aware clustering improving FlashAttention 4 throughput by up to 5.30% over its baseline. We further show that, under thermal design power (TDP) limits, reduced data movement enables higher Streaming Multiprocessor (SM) frequencies through Dynamic Voltage and Frequency Scaling (DVFS), explaining otherwise unexpected throughput improvements. Extending this analysis beyond GPUs, we show that power constraints also limit throughput scaling on Tenstorrent's Wormhole accelerator, highlighting the broader importance of accounting for physical non-idealities when analyzing and optimizing kernel performance.
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v1 · 2026-10-28 00:00 · 最新观测版本
Kernel Performance Engineering under Physical Non-Idealities
Physical non-idealities in integrated circuit (IC) design and fabrication are concealed by conventional hardware abstractions, yet they can limit kernel performance and obscure optimization opportunities. Using distributed shared memory (DSMEM) on NVIDIA H100 GPUs as a case study, we show how floorsweeping restricts hardware resources available for clustered execution, potentially offsetting the benefits of reduced data movement enabled by DSMEM. Guided by these insights, we develop a wave-aware clustering strategy that accounts for these resource constraints to improve kernel performance. Evaluations using a global-load-and-broadcast microbenchmark, GEMM, and the Hopper version of FlashAttention 4 demonstrate the performance implications of these constraints, with wave-aware clustering improving FlashAttention 4 throughput by up to 5.30% over its baseline. We further show that, under thermal design power (TDP) limits, reduced data movement enables higher Streaming Multiprocessor (SM) frequencies through Dynamic Voltage and Frequency Scaling (DVFS), explaining otherwise unexpected throughput improvements. Extending this analysis beyond GPUs, we show that power constraints also limit throughput scaling on Tenstorrent's Wormhole accelerator, highlighting the broader importance of accounting for physical non-idealities when analyzing and optimizing kernel performance.
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