Adaptive sampling for performance characterization of application kernels

Characterizing performance is essential to optimize programs and architectures. The open source Adaptive Sampling Kit (ASK) measures the performance trade-off in large design spaces. Exhaustively sampling all sets of parameters is computationally intractable. Therefore, ASK concentrates exploration in the most irregular regions of the design space through multiple adaptive sampling strategies. 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 three performance characterization problems: memory stride accesses, jacobian stencil code and an industrial seismic application using 3D stencils. ASK builds accurate models of performance with a small number of measures. It considerably reduces the cost of performance exploration. For instance, the jacobian stencil code design space, which has more than 31 × 10 combinations of parameters, is accurately predicted using only 1 500 combinations.