Demystifying the Impact of Physical Non-Idealities on Kernel Performance

arXiv:2610.37466v2 Announce Type: replace Abstract: Physical non-idealities in integrated circuit (IC) design, fabrication, and operation are concealed by conventional hardware abstractions, causing unexpected performance variations in kernels. For large-scale workloads, such as AI training and inference, performance unpredictability can lead to substantial losses. We investigate this problem in NVIDIA H100 GPUs with their distributed shared memory (DSMEM) architectural feature that reduces data movement from the L2 cache to Streaming Multiprocessors (SMs) with multicast loads, and uncover that floorsweeping restricts the number of SMs usable for clustered execution, increasing the number of execution waves of kernels.