为ARM SVE架构优化数据科学库，性能超x86平台且成本更低。
oneDAL Optimization for ARM Scalable Vector Extension: Maximizing Efficiency for High-Performance Data Science
- 用OpenBLAS替代MKL，实现oneDAL在ARM SVE上的移植与优化。
- SVE优化的SVM算法提升22%（Boser）和5%（Thunder）性能，最高提速200倍。
- 性能媲美甚至超过昂贵x86系统，适合高性价比数据科学场景。
ARM架构结合可扩展向量扩展（SVE）为高性能计算（HPC）和机器学习（ML）带来新机遇。统一加速基础（UXL）的oneAPI数据分析库（oneDAL）虽广泛用于加速数据分析，但依赖英特尔专有数学内核库（MKL），长期仅支持x86平台。本文实现oneDAL在ARM SVE架构的移植，采用OpenBLAS作为替代后端，并引入自定义稀疏矩阵运算、向量化统计函数及SVE优化的支撑向量机（SVM）算法。SVM利用SVE灵活向量长度和谓词执行机制，实现Boser方法22%、Thunder方法5%的性能提升。在搭载SVE的AWS Graviton3实例上，相比原生scikit-learn实现，机器学习训练与推理任务最高提速200倍。优化后的ARM oneDAL在性能上达到甚至超越基于IceLake x86系统的MKL版oneDAL，而后者成本接近前者的两倍。结果表明，ARM是数据密集型机器学习应用兼具高性能与能效的有力候选平台。该工作拓展了跨架构兼容性，推动开源生态发展，强化了ARM在HPC与机器学习领域的竞争力。
原文摘要 · Abstract (English)
The evolution of ARM-based architectures, particularly those incorporating Scalable Vector Extension (SVE), has introduced transformative opportunities for high-performance computing (HPC) and machine learning (ML) workloads. The Unified Acceleration Foundation's (UXL) oneAPI Data Analytics Library (oneDAL) is a widely adopted library for accelerating ML and data analytics workflows, but its reliance on Intel's proprietary Math Kernel Library (MKL) has traditionally limited its compatibility to x86platforms. This paper details the porting of oneDAL to ARM architectures with SVE support, using OpenBLAS as an alternative backend to overcome architectural and performance challenges. Beyond porting, the research introduces novel ARM-specific optimizations, including custom sparse matrix routines, vectorized statistical functions, and a Scalable Vector Extension (SVE)-optimized Support Vector Machine (SVM) algorithm. The SVM enhancements leverage SVE's flexible vector lengths and predicate driven execution, achieving notable performance gains of 22% for the Boser method and 5% for the Thunder method. Benchmarks conducted on ARM SVE-enabled AWSGraviton3 instances showcase up to 200x acceleration in ML training and inference tasks compared to the original scikit-learn implementation on the ARM platform. Moreover, the ARM-optimized oneDAL achieves performance parity with, and in some cases exceeds, the x86 oneDAL implementation (MKL backend) on IceLake x86 systems, which are nearly twice as costly as AWSGraviton3 ARM instances. These findings highlight ARM's potential as a high-performance, energyefficient platform for dataintensive ML applications. By expanding cross-architecture compatibility and contributing to the opensource ecosystem, this work reinforces ARM's position as a competitive alternative in the HPC and ML domains, paving the way for future advancements in dataintensive computing.
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