Gaussian processes are widely used in machine learning domains but remain computationally demanding, limiting their efficient scalability across diverse hardware platforms. The GPRat library targets these challenges with the help of the asynchronous many-task runtime system HPX. In this work, we extend GPRat to enable portability across multiple hardware architectures and evaluate its performance on representative x86-64, ARM, and RISC-V chips. We conduct node-level strong-scaling and problem-size-scaling benchmarks for Gaussian Process prediction and hyperparameter optimization to assess single-core performance, parallel scalability, and architectural efficiency. Our results show that while the x86-64 Zen 2 chip achieves a 58% single-core performance advantage over the ARM-based Fujitsu A64FX, superior parallel scaling allows the 48-core ARM chip to outperform the 64-core Zen 2 by 9% at full node utilization. The evaluated SOPHON SG2042 RISC-V chip exhibits substantially lower performance and weaker scalability, with single-core performance lagging by up to a factor of 14 and large-scale parallel workloads showing slowdowns of up to a factor of 25. For problem-size scaling, ARM and x86-64 systems demonstrate comparable performance within 25%. These findings highlight the growing competitiveness of ARM-based processors and emphasize the importance of wide-register vectorization support and memory subsystem improvements for upcoming RISC-V platforms.

翻译：高斯过程广泛应用于机器学习领域，但其计算密集性限制了其在多样硬件平台上的高效可扩展性。GPRat库借助异步多任务运行时系统HPX应对这些挑战。在本工作中，我们扩展GPRat以支持跨多种硬件架构的可移植性，并在代表性的x86-64、ARM和RISC-V芯片上评估其性能。我们针对高斯过程预测与超参数优化，开展了节点级强扩展与问题规模扩展基准测试，以评估单核性能、并行可扩展性及架构效率。结果表明：尽管x86-64 Zen 2芯片的單核性能比基于ARM的Fujitsu A64FX高58%，但优异的并行扩展能力使48核ARM芯片在节点满载时比64核Zen 2芯片性能高9%。受测的SOPHON SG2042 RISC-V芯片性能显著较低且可扩展性较弱，其单核性能差距高达14倍，大规模并行工作负载的减速比可达25倍。在问题规模扩展方面，ARM与x86-64系统的性能差异在25%以内。这些发现凸显了ARM处理器的竞争力日益增强，同时强调了宽向量化支持与内存子系统改进对下一代RISC-V平台的重要性。