Racer: Hunting GPU Kernel Bugs with Symbolic Equivalence Checking
GPU kernels are critical to the performance of today's AI services, yet the same operators are rewritten repeatedly: for each GPU generation, numeric format, serving regime, and kernel language. Every rewrite must preserve the function computed by the implementation it replaces. Today, this obligation depends almost entirely on differential testing: run the new kernel and a trusted reference on randomly sampled tensors, then compare their outputs within a tolerance. This approach has blind spots that additional test cases cannot eliminate. For example, a logical bug may produce an error smaller than the tolerance required to accommodate a correct optimized kernel. We propose symbolic equi...
| Authors | Tuan Truong Vu, Cheng Tan |
|---|---|
| Primary category | cs.SC |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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