GPU Kernel Optimization

GPU kernel optimization studies how to implement individual operations—such as matrix multiplication, attention, reductions, and sparse computations—so they use GPU memory and compute units efficiently. Common approaches include tiling and fusion, low-precision arithmetic, compiler scheduling, and domain-specific languages such as CUDA, Triton, and CuTe. Autotuners and LLM-based agents increasingly search these design spaces using profiling, execution feedback, evolutionary search, or reinforcement learning, with benchmarks such as KernelBench measuring correctness and speed. A central challenge is that gains often depend on the workload and GPU architecture: generated kernels may pass correctness checks yet lose to vendor libraries, regress on unseen shapes, or yield little end-to-end improvement.