Using kernel couplings to predict parallel application performance

• Published in: 11th International Symposium on High-Performance Distributed Computing (HPDC-11 2002) pp. 125 - 134
• Main Authors: Taylor, V., Xingfu Wu, Geisler, J., Stevens, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2002
• Subjects: Algebra; Analytical models; Application software; Computer science; Kernel; Laboratories; Mathematical model; Mathematics; Performance analysis; Predictive models
• ISBN: 0769516866; 9780769516868
• ISSN: 1082-8907
• DOI: 10.1109/HPDC.2002.1029910

Performance models provide significant insight into the performance relationships between an application and the system used for execution. The major obstacle to developing performance models is the lack of knowledge about the performance relationships between the different functions that compose an application. This paper addresses the issue by using a coupling parameter, which quantifies the interaction between kernels, to develop performance predictions. The results, using three NAS parallel application benchmarks, indicate that the predictions using the coupling parameter were greatly improved over a traditional technique of summing the execution times of the individual kernels in an application. In one case the coupling predictor had less than 1% relative error in contrast the summation methodology that had over 20% relative error. Further, as the problem size and number of processors scale, the coupling values go through a finite number of major value changes that is dependent on the memory subsystem of the processor architecture.