GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization 事件

GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization arXiv:2605.31464v1 Announce Type: cross Abstract: GPU kernels are the workhorse of modern deep learning, and optimizing them (via evolutionary search or coding agents) usually requires repeated measurement on target hardware. While these measurements provide the ground-truth signal necessary for kernel search, they are costly, because each evaluation of a kernel requires compilation and repeated execution o