Les problèmes qui tirent parti du GPU

GPUs are powerful, but they shine on a specific shape of problem. Learning to spot that shape saves you a lot of wasted effort. 🔍

The dream case is embarrassingly parallel work: many items, each handled the same way, with no item depending on another.

If you can say apply this same step to every element, you have a great fit. That regular pattern maps perfectly onto thousands of threads.

The best GPU tasks have independent work units. When element 5 does not need element 4's result, threads run freely without waiting.

Image filters are a classic fit: each pixel is brightened or blurred on its own, so millions of them can be processed at the same time. 🖼️

Adding, scaling, or multiplying huge vectors and matrices is ideal. Each output depends only on a few inputs, perfect for parallel cores.

Neural networks are mostly giant matrix multiplications repeated millions of times. That heavy, regular math is exactly the GPU's sweet spot.

Tasks full of dependencies, where each step needs the previous one, fight the GPU. There is little to run in parallel, so cores sit idle.

Code with heavy branching and irregular control flow causes warp divergence, so the GPU loses much of its speed advantage there.

Even a perfect task can flop if copying data to the GPU costs more than the work saved. Always weigh that transfer overhead first.

Ask: many items, the same operation, and little dependency between them? If yes, the GPU will likely give you a big win.

Let us test your eye for GPU-friendly work.

GPUs love embarrassingly parallel work: many items, the same operation, little dependency. Mind data transfer cost and avoid branchy logic. 👍