Parameterized Micro-benchmarking: An Auto-tuning Approach for Complex Applications

Wenjing Ma, S. Krishnamoorthy, G. Agrawal
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引用次数: 5

Abstract

Auto-tuning has emerged as an important practical method for creating highly optimized code. However, the growing complexity of architectures and applications has resulted in a prohibitively large search space that preclude empirical auto-tuning. Here, we focus on the challenge to auto-tuning presented by applications that require auto-tuning of not just a small number of distinct kernels, but a large number of kernels that exhibit similar computation and memory access characteristics and require optimization over similar problem spaces. We propose an auto-tuning method for tensor contraction functions on GPUs, based on parameterized micro-benchmarks. Using our parameterized micro-benchmarking approach, we obtain a speedup of up to 2 over the version that used default optimizations without auto-tuning.
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参数化微基准测试:复杂应用的自动调优方法
自动调优已经成为创建高度优化代码的重要实用方法。然而,体系结构和应用程序日益复杂,导致搜索空间过大,无法进行经验自动调优。这里,我们将重点关注应用程序所面临的自动调优挑战,这些应用程序不仅需要对少量不同的内核进行自动调优,而且需要对大量具有相似计算和内存访问特征的内核进行自动调优,并且需要对类似的问题空间进行优化。我们提出了一种基于参数化微基准的gpu张量收缩函数的自动调优方法。使用我们的参数化微基准测试方法,与使用默认优化而不进行自动调优的版本相比,我们获得了高达2倍的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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