SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits
Edward Lin
Sahil Modi
Siva Kumar Sastry Hari
Qijing Huang
Zhifan Ye
Nestor Qin
Fengzhe Zhou
Yuan Zhang
Jingquan Wang
Sana Damani
Dheeraj Peri
Ouye Xie
Aditya Kane
Moshe Maor
Michael Behar
Triston Cao
Rishabh Mehta
Vartika Singh
Vikram Sharma Mailthody
Terry Chen
Zihao Ye
Hanfeng Chen
Tianqi Chen
Vinod Grover
Wei Chen
Wei Liu
Eric Chung
Luis Ceze
Roger Bringmann
Cyril Zeller
Michael Lightstone
Humphrey Shi
Preprint, 2026
Abstract
As agentic AI systems become increasingly capable of generating and optimizing GPU kernels, progress is constrained by benchmarks that reward speedup over software baselines rather than proximity to hardware-efficient execution. We present SOL-ExecBench, a benchmark of 235 CUDA kernel optimization problems extracted from 124 production and emerging AI models spanning language, diffusion, vision, audio, video, and hybrid architectures, targeting NVIDIA Blackwell GPUs. The benchmark covers forward and backward workloads across BF16, FP8, and NVFP4, including kernels whose best performance is expected to rely on Blackwell-specific capabilities. Unlike prior benchmarks that evaluate kernels primarily relative to software implementations, SOL-ExecBench measures performance against analytically derived Speed-of-Light (SOL) bounds computed by SOLAR, our pipeline for deriving hardware-grounded SOL bounds, yielding a fixed target for hardware-efficient optimization. We report a SOL Score that quantifies how much of the gap between a release-defined scoring baseline and the hardware SOL bound a candidate kernel closes. To support robust evaluation of agentic optimizers, we additionally provide a sandboxed harness with GPU clock locking, L2 cache clearing, isolated subprocess execution, and static analysis based checks against common reward-hacking strategies. SOL-ExecBench reframes GPU kernel benchmarking from beating a mutable software baseline to closing the remaining gap to hardware Speed-of-Light.