AcceleratedKernels.jl is introduced as a backend-agnostic library for parallel computing in Julia, natively targeting NVIDIA, AMD, Intel, and Apple accelerators via a unique transpilation architecture. Written in a unified, compact codebase, it enables productive parallel programming with minimised implementation and usage complexities. Benchmarks of arithmetic-heavy kernels show performance on par with C and OpenMP-multithreaded CPU implementations, with Julia sometimes offering more consistent and predictable numerical performance than conventional C compilers. Exceptional composability is highlighted as simultaneous CPU-GPU co-processing is achievable - such as CPU-GPU co-sorting - with transparent use of hardware-specialised MPI implementations. Tests on the Baskerville Tier 2 UK HPC cluster achieved world-class sorting throughputs of 538-855 GB/s using 200 NVIDIA A100 GPUs, comparable to the highest literature-reported figure of 900 GB/s achieved on 262,144 CPU cores. The use of direct NVLink GPU-to-GPU interconnects resulted in a 4.93x speedup on average; normalised by a combined capital, running and environmental cost, communication-heavy HPC tasks only become economically viable on GPUs if GPUDirect interconnects are employed.
@article{arxiv.2507.16710,
title = {AcceleratedKernels.jl: Cross-Architecture Parallel Algorithms from a Unified, Transpiled Codebase},
author = {Andrei-Leonard Nicusan and Dominik Werner and Simon Branford and Simon Hartley and Andrew J. Morris and Kit Windows-Yule},
journal= {arXiv preprint arXiv:2507.16710},
year = {2025}
}