|Title||Scalability Prediction for Fundamental Performance Factors|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Rosas, C, Giménez, J, Labarta, J|
|Journal||Supercomputing Frontiers and Innovation|
|Keywords||analysis and prediction, curve-fitting, exascale computing, parallel efficiency|
Inferring the expected performance for parallel applications is getting harder than ever; applications need to be modeled for restricted or nonexistent systems and performance analysts are required to identify and extrapolate their behavior using only the available resources. Prediction models can be based on detailed knowledge of the application algorithms or on blindly trying to extrapolate measurements from existing architectures and codes. This paper describes the work done to define an intermediate methodology where the combination of (a) the essential knowledge about fundamental factors in parallel codes, and (b) detailed analysis of the application behavior at low core counts on current platforms, guides the modeling efforts to estimate behavior at very large core counts. Our methodology integrates the use of several components like instrumentation package, visualization tools, simulators, analytical models and very high level information from the application running on systems in production to build a performance model.
Submitted by toolsweb on Tue, 05/03/2016 - 16:37