A Developer Explores Triton to Write Custom GPU Kernels Beyond PyTorch Abstractions

A developer explored Triton, a Python-based language and compiler for writing GPU kernels, to better understand what happens when ML code runs on a GPU. Standard PyTorch abstractions like torch.softmax() are convenient but can trigger separate kernel launches and repeated global memory reads and writes, creating hidden performance costs. Triton addresses this by letting programmers think in terms of data blocks rather than individual GPU threads, with the compiler handling how work is distributed across the hardware. Understanding the GPU memory hierarchy — from fast registers to slower global memory — proved essential, as minimizing unnecessary data movement is key to performance. The developer began with a simple vector addition exercise in Triton, which illustrated the block-based programming model and how each program instance can process a chunk of data rather than a single element.