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Erschienen in:
2012 | OriginalPaper | Buchkapitel
ASK: Adaptive Sampling Kit for Performance Characterization
verfasst von : Pablo de Oliveira Castro, Eric Petit, Jean Christophe Beyler, William Jalby
Erschienen in: Euro-Par 2012 Parallel Processing
Verlag: Springer Berlin Heidelberg
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Characterizing performance is essential to optimize programs and architectures. The open source Adaptive Sampling Kit (ASK) measures the performance trade-offs in large design spaces. Exhaustively sampling all points is computationally intractable. Therefore, ASK concentrates exploration in the most irregular regions of the design space through multiple adaptive sampling methods. The paper presents the ASK architecture and a set of adaptive sampling strategies, including a new approach: Hierarchical Variance Sampling. ASK’s usage is demonstrated on two performance characterization problems: memory stride accesses and stencil codes. ASK builds precise models of performance with a small number of measures. It considerably reduces the cost of performance exploration. For instance, the stencil code design space, which has more than 31.10
8
points, is accurately predicted using only 1 500 points.