Adaptive Sampling for Performance Characterization of Application Kernels - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Concurrency and Computation: Practice and Experience Année : 2013

Adaptive Sampling for Performance Characterization of Application Kernels

Résumé

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 called 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 × 10^8 combinations of parameters, is accurately predicted using only 1500 combinations.

Dates et versions

hal-00952288 , version 1 (26-02-2014)

Identifiants

Citer

Pablo de Oliveira Castro, Eric Petit, Asma Farjallah, William Jalby. Adaptive Sampling for Performance Characterization of Application Kernels. Concurrency and Computation: Practice and Experience, 2013, 25 (17), pp.2345-2362. ⟨10.1002/cpe.3097⟩. ⟨hal-00952288⟩

Collections

CNRS INSMI UVSQ
128 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More