SPAPT: Search Problems in Automatic Performance Tuning

Abstract

Automatic performance tuning of computationally intensive kernels in scientific applications is a promising approach to achieving good performance on different machines while preserving the kernel implementation's readability and portability. A major bottleneck in automatic performance tuning is the computation time required to test a large number of possible code variants, which grows exponentially with the number of tuning parameters. Consequently, the design, development, and analysis of effective search techniques capable of quickly finding high-performing parameter configurations have gained significant attention in recent years. An important element needed for this research is a collection of test problems that allow performance engineering and mathematical optimization researchers to conduct rigorous algorithmic development and experimental studies. In this paper, we describe a set of extensible and portable search problems in automatic performance tuning (SPAPT) whose goal is to aid in the development and improvement of search strategies. SPAPT is a test suite that contains representative serial code implementations from a number of lower-level performance-tuning tasks in scientific applications. We present an illustrative experimental study on several problems from the test suite. We discuss important issues such as modeling, search space characteristics, and performance objectives.