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.
Original language | English |
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Pages (from-to) | 1959-1968 |
Number of pages | 10 |
Journal | Procedia Computer Science |
Volume | 9 |
DOIs | |
State | Published - 2012 |
Externally published | Yes |
Event | 12th Annual International Conference on Computational Science, ICCS 2012 - Omaha, NB, United States Duration: Jun 4 2012 → Jun 6 2012 |
Funding
This work was supported by the Office of Advanced Scientific Computing Research, Office of Science, U.S. Department of Energy, under Contract DE-AC02-06CH11357. ∗Corresponding author Email addresses: [email protected] (Prasanna Balaprakash), [email protected] (Stefan M. Wild), [email protected] (Boyana Norris) This work was supported in part by the Office of Advanced Scientific Computing Research, Office of Science, U.S. Dept. of Energy, under Contract DE-AC02-06CH11357. We are grateful to Paul D. Hovland for helpful discussions and to the Laboratory Computing Resource Center, Leadership Computing Facility at Argonne National Laboratory and the National Energy Research Scientific Computing Center.
Keywords
- Autotuning
- Benchmark
- Empirical tuning
- Kernels
- Optimization
- Performance tuning
- Test suite