Hardware performance monitoring for the rest of us: a position and survey
NPC'11 Proceedings of the 8th IFIP international conference on Network and parallel computing
Quantifying performance bottleneck cost through differential analysis
Proceedings of the 27th international ACM conference on International conference on supercomputing
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In performance critical applications, memory latency is frequently the dominant overhead. In many cases, automatic compiler-based optimizations to improve memory performance are limited and programmers frequently resort to manual optimization techniques. However, this process is tedious and time-consuming. Furthermore, as the potential benefit from optimization is unknown there is no way to judge the amount of effort worth expending, nor when the process can stop, i.e. when optimal memory performance has been achieved or sufficiently approached. Architecture simulators can provide such information but designing an accurate model of an existing architecture is difficult and simulation times are excessively long. In this article, we propose and implement a technique that is both fast and reasonably accurate for estimating a lower bound on execution time for scientific applications. This technique has been tested on a wide range of programs from the SPEC benchmark suite and two commercial applications, where it has been used to guide a manual optimization process and iterative compilation. We compare our technique with that of a simulator with an ideal memory behaviour and demonstrate that our technique provides comparable information on memory performance and yet is over two orders of magnitude faster. We further show that our technique is considerably more accurate than hardware counters. Copyright © 2004 John Wiley & Sons, Ltd.