gpu-compilation

TrainBridge is an agentic framework that transfers GPU compilation optimization guidance to Ascend NPU training programs, achieving an average 1.7× speedup across four workloads and nearly 2.0× across 27 Modded-NanoGPT speedrun records by extracting optimization intents from GPU executions and iteratively refining NPU programs through profiling.

The Futhark team explains why their compiler's GPU pipelines force all arrays into GPU memory, making sequential CPU-side work (like greedy distance-2 graph colouring for sparse Jacobians) three orders of magnitude slower, and describes a hack that allows keeping some functions off the GPU while still exploiting GPU parallelism for the rest of the program.