用机器学习自动选线程数，让矩阵乘法快25%~40%。
A Machine Learning Approach Towards Runtime Optimisation of Matrix Multiplication
- 用机器学习模型实时预测最优线程数
- 在100MB内存下提速25%~40%
- 适合高性能计算中频繁调用GEMM的场景
通用矩阵乘法（GEMM）是科学计算的核心算法。单线程GEMM已通过分块和自调优等技术高度优化，但现代多核共享内存系统复杂，难以确定使多线程GEMM运行时间最短的线程数。本文提出一种概念验证方法，构建架构与数据结构感知的线性代数库（ADSALA），利用机器学习优化BLAS函数的运行时性能。具体而言，该方法基于训练数据，在运行时使用机器学习模型自动为给定的GEMM任务选择最优线程数。在两种不同高性能计算节点架构上测试：一个基于双路Intel Cascade Lake，另一个基于双路AMD Zen 3。结果显示，当GEMM内存占用在100MB以内时，相比传统BLAS实现，速度提升25%至40%。
原文摘要 · Abstract (English)
The GEneral Matrix Multiplication (GEMM) is one of the essential algorithms in scientific computing. Single-thread GEMM implementations are well-optimised with techniques like blocking and autotuning. However, due to the complexity of modern multi-core shared memory systems, it is challenging to determine the number of threads that minimises the multi-thread GEMM runtime. We present a proof-of-concept approach to building an Architecture and Data-Structure Aware Linear Algebra (ADSALA) software library that uses machine learning to optimise the runtime performance of BLAS routines. More specifically, our method uses a machine learning model on-the-fly to automatically select the optimal number of threads for a given GEMM task based on the collected training data. Test results on two different HPC node architectures, one based on a two-socket Intel Cascade Lake and the other on a two-socket AMD Zen 3, revealed a 25 to 40 per cent speedup compared to traditional GEMM implementations in BLAS when using GEMM of memory usage within 100 MB.
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