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Iterative Optimization for the Data Center by Yang Chen; Shuangde Fang; Lieven Eeckhout; Olivier Temam; Chengyong Wu is a Computer Science article available to read on EtoBox.

What is Iterative Optimization for the Data Center about?

Iterative optimization is a simple but powerful approach that searches for the best possible combination of compiler optimizations for a given workload. However, each program, if not each data set, potentially favors a different combination. As a result, iterative optimization is plagued by several practical issues that prevent it from being widely used in practice: a large number of runs are required for finding the best combination; the process can be data set dependent; and the exploration process incurs significant overhead that needs to be compensated for by performance benefits.Therefore, while iterative optimization has been shown to have significant performance potential, it is seldomly used in production compilers. In this paper, we propose Iterative Optimization for the Data Center (IODC): we show that servers and data centers offer a context in which all of the above hurdles can be overcome. The basic idea is to spawn different combinations across workers and recollect performance statistics at the master, which then evolves to the optimum combination of compiler optimizations. IODC carefully manages costs and benefits, and is transparent to the end user. We evaluate IOD

Who reads Iterative Optimization for the Data Center?

Author

Yang Chen; Shuangde Fang; Lieven Eeckhout; Olivier Temam; Chengyong Wu

Publisher

ACM

Published

2012

Language

EN

Field

Computer Science (Physical Sciences)