Achieving near native runtime performance and cross-platform performance portability for random number generation through SYCL interoperability

TL;DR

This paper demonstrates that using SYCL interoperability enables scientific applications, specifically random number generation, to achieve near-native runtime performance across multiple hardware platforms, simplifying cross-platform HPC development.

Contribution

The paper introduces NVIDIA and AMD random number generator extensions for oneMKL, showcasing SYCL interoperability for cross-platform performance portability in scientific computing.

Findings

SYCL-based implementations match native performance levels

Extensions enable cross-platform compatibility for random number generation

Performance measured across four major hardware platforms

Abstract

High-performance computing (HPC) is a major driver accelerating scientific research and discovery, from quantum simulations to medical therapeutics. While the increasing availability of HPC resources is in many cases pivotal to successful science, even the largest collaborations lack the computational expertise required for maximal exploitation of current hardware capabilities. The need to maintain multiple platform-specific codebases further complicates matters, potentially adding constraints on machines that can be utilized. Fortunately, numerous programming models are under development that aim to facilitate portable codes for heterogeneous computing. One in particular is SYCL, an open standard, C++-based single-source programming paradigm. Among SYCL's features is interoperability, a mechanism through which applications and third-party libraries coordinate sharing data and execute collaboratively. In this paper, we leverage the SYCL programming model to demonstrate cross-platform performance portability across heterogeneous resources. We detail our NVIDIA and AMD random number generator extensions to the oneMKL open-source interfaces library. Performance portability is measured relative to platform-specific baseline applications executed on four major hardware platforms using two different compilers supporting SYCL. The utility of our extensions are exemplified in a real-world setting via a high-energy physics simulation application. We show the performance of implementations that capitalize on SYCL interoperability are at par with native implementations, attesting to the cross-platform performance portability of a SYCL-based approach to scientific codes.