GPU & acceleration

How Eugo moves heavy numeric work onto GPUs without code changes.

Courses

How work reaches the GPU, which operations benefit, and how to confirm it actually happened.

Eugo rewrites and tunes your code as it runs. Here is what it does, and how to help it.

Use .options() to ask for GPUs on the calls that need them, and only those.

Which array operations Eugo moves to the GPU on its own, and how to confirm it happened.

Work through the usual causes when a distributed run is slower than expected.

GPUs, per-call resource requests, and which optimizations the runtime applies on your behalf, plus how to confirm work actually reached the GPU.

The terms this subject uses, defined.

ARM Neoverse : The ARM server processor architecture Eugo’s compute nodes are built on, chosen for performance per watt on numeric workloads.
GPU offloading : Moving computationally intensive operations onto a GPU. Eugo applies this automatically when a GPU is available and the data transfer is worth its cost.
GPU : A graphics processing unit. Hardware with many parallel cores, well suited to large array and matrix operations.
options() : The method that sets per-call resource requirements on a distributed function, such as num_cpus or num_gpus.
Automatic optimization : Transformations Eugo applies to your code as it runs: vectorization, GPU offloading, and low-level tuning, with no annotation from you.
Transfer cost : The time spent moving data between machines, or between host and GPU memory. Often the reason an operation is not worth accelerating.