GPU Kernel Programming
GPU kernel programming implements numerical operations as parallel work executed on a graphics processor. Practitioners choose thread and memory layouts, combine operations and handle numerical and shape constraints, aiming to reduce actual execution time while preserving the required computation across representative inputs and hardware.
What it is
A kernel describes work executed by many parallel processing units. Performance depends on how data is loaded, reused and synchronized, as well as arithmetic throughput. GPU memory hierarchies make unnecessary transfers expensive; fusion can keep intermediate values on-chip instead of writing them between operations. Languages and toolchains such as CUDA and Triton expose different levels of control. This differs from merely moving a tensor operation to a GPU through a framework. Kernel programming changes the operation's implementation, requiring attention to bounds, reductions, synchronization and numerical behavior as well as the algorithm's mathematical expression.
What the work involves
The practitioner profiles the workload to locate a meaningful bottleneck, implements a reference-equivalent kernel and tests shapes, strides and edge cases. Tiling and launch parameters are tuned on the actual target hardware. Useful outputs include the kernel, correctness checks and a benchmark that accounts for asynchronous execution and warmup. End-to-end measurements determine whether a local improvement matters to the application. A maintainable fallback is useful when a custom kernel cannot support all required shapes or devices.
Illustrative example
A preprocessing step computes row-wise normalization through several framework operations. A developer writes a fused kernel that loads each row, computes a stable reduction and writes the normalized values without materializing every intermediate tensor. Tests include irregular row lengths, empty handling and extreme values. Benchmarks compare the fused and reference implementations across the service's real shapes, and an application test checks whether preprocessing was significant enough for the change to affect request latency.
Limits and common mistakes
A fast kernel for one shape can be slower or incorrect for another. Register pressure, shared-memory use and synchronization can defeat expected gains from fusion. Reduced precision and approximate functions require explicit tolerances. Quality includes validated numerical behavior and realistic measurements, not theoretical operation counts alone. Custom kernels add hardware and compiler dependencies, so the maintenance cost should be justified by a demonstrated bottleneck that existing optimized libraries cannot adequately address.
Prerequisites
- mediumInference Optimization
Custom kernels are an inference/training speedup.
- softLinear Algebra
Kernels implement tensor math.
Related skills
- ← is an instance of: CUDA
- ← is subcategory of: GPU acceleration
Sources and further reading
- Triton fused softmax tutorial
Demonstrates fusion, on-chip reuse, masked memory access, numerical checks and shape-dependent GPU benchmarking.
Last updated: 2026-10-10