GPU Performance Engineering: A Practical Learning Path

GPU performance engineering means explaining where an operation spends time, changing the part that limits it, and proving that the result remains correct. Start with the execution model and memory hierarchy, then learn layouts, tiling, compilation and work partitioning. Finish with an experiment that preserves inputs, correctness checks, timing boundaries and a relevant baseline. This page connects those steps to courses, references, interview problems and runnable projects.

You do not need a cluster to begin. Some important errors can be found with six integers and a pencil. Hardware becomes necessary when you want to establish device behavior or speed. Keeping those kinds of evidence separate makes your work easier to review.

Choose an entry point you can complete

If arrays, tensor shapes and a training loop are still unfamiliar, begin with the free tiny language model project. It runs on a CPU and provides an inspectable training loop. Its educational checkpoint is not a production model.

If you already understand tensor operations, use AI Infrastructure Foundations for the memory and performance budgets, then continue with Kernels and the Machine. The kernel course has seven modules and 21 lessons/projects. Its advanced lessons assume comfort reading code and reasoning about arrays.

The roadmap is public. The new modules, concepts and two projects below are Premium resources, with access shown on their pages.

Learn to name the memory boundary

A matrix-multiplication kernel can issue repeated global loads that hit in cache. Counting those load requests and calling them HBM traffic gives the wrong bandwidth estimate. Tiled Matrix Multiplication derives the difference between a block's staged operand requests and an ideal whole-operation device-memory bound.

The distinction changes what you optimize. Coalescing can reduce transactions without changing the number of useful values. Tiling can reduce repeated requests. Fusion can remove an intermediate tensor. None of those statements proves which boundary limited your application; you need a trace and measurements that match the proposed mechanism.

The course develops reductions, tiled matrix multiplication and online attention after the execution and memory lessons. Read the CUDA Programming Guide alongside that work for the platform's execution and synchronization rules. Keep architecture-specific instruction and resource limits in your experiment record.

Follow values through layouts and compiled artifacts

A transpose view can change logical coordinates while retaining the same storage. Lane assignment adds another mapping, and shared-memory layouts add another. Before trying a sophisticated pipeline, trace one coordinate to its element offset and then identify the producer and consumer that own it.

The new work-mapping module begins with the kernel that actually runs. PTX, native code and source are distinct artifacts. A first-call delay can include compilation and initialization; a warmed kernel timer measures a narrower interval. An optimization claim should identify which one improved.

Next, map coordinates before moving bytes and balance the grid before tuning the tile. These lessons connect layouts and buffer lifetimes to GEMM work partitioning. The CuTe layout documentation provides a primary reference for the coordinate-to-index model.

Use small models to expose ownership errors

The CPU work-mapping project needs only Python. It models scheduling waves, transpose offsets, prefix-scan carries and softmax state merging, with 13 executed tests.

For counts [3,0,2,1,0,4], two blocks can each compute correct local offsets and still overlap their writes. The second block needs the first block's total before it can own a disjoint range. The project shows all intermediate arrays and includes a deliberately broken missing-carry calculation. You can inspect the failure without a profiler.

Those models are useful preparation for a GPU implementation. They do not validate a device-wide barrier, cache behavior or a speed claim. The parallel prefix scan reference makes that boundary explicit.

Make correctness part of the experiment

A kernel that works for 256 elements can still fail for 257. A test that compares two arrays can miss input mutation or accept an unwritten output under a careless NaN policy. An evaluator needs its own tests.

Kernel Correctness Contracts starts with the supported operation, layout, dtype and numerical domain. The course then asks you to choose adversarial inputs and explain what each diagnostic can establish. Compute Sanitizer addresses classes of memory and synchronization errors; it does not replace an application-level numerical reference.

This procedure also applies to generated kernels. Review source before executing it, keep the evaluator independent, preserve failed cases and require known broken candidates to fail. Neither a familiar-looking kernel nor a large speedup makes the result correct.

Measure against a baseline worth beating

The Triton qualification project implements residual addition followed by ReLU. It checks 27 custom-kernel cases, rejects two broken implementations and five unsupported-input cases, and compares eager, compiled and custom paths across three shapes.

The project ran on an RTX 5070. The first run showed why the compiled baseline matters: custom Triton beat eager execution, but compiled PyTorch was faster at the largest shape. A second run put the two close together and reversed their median order. The guide keeps that result, raw-round methodology and limitations visible. It does not turn a desktop microbenchmark into a service-throughput claim.

Your final deliverable is a short decision memo. State the input domain, artifact identity, correctness evidence, timing boundary and result by shape. Explain what would reverse your recommendation. Include a deployment or maintenance consideration if you are proposing production use.

Connect kernel work to the service

A kernel that consumes 20% of a sequential workload cannot make the complete workload twice as fast. Even making that component instantaneous would cap the ideal speedup at 1 / 0.8 = 1.25×, assuming everything else stays fixed. This arithmetic helps decide whether further kernel work deserves the time.

Use Inference Engineering to connect the operation to batching, request latency, cache management and capacity. For interview practice, work through the partial-wave GEMM decision, overlapping output ranges and qualifying a generated kernel. These are authored engineering exercises, not claims about questions asked by a particular employer.

Keep one experiment small enough to reproduce. A complete record that explains why a compiled framework implementation won is useful engineering work, and it gives you a sound starting point for the next optimization.