A Refinement Strategy for a User-Oriented Performance Analysis

We introduce a refinement strategy to bring the parallel performance analysis closer to the user. The analysis starts with a simple high-level performance model. It is based on first-order approximations, in terms of the logical constituents of the parallel program and characteristics of the system. This model is then progressively refined with more detailed low-level performance aspects, to explain divergences from a ’normal’, linear regime. We use a causal model to structure the relations between all variables involved. The approach intends to serve as a link between detailed performance data and the developer. It is demonstrated with a parallel matrix multiplication algorithm.

[1]  Thomas J. LeBlanc,et al.  Parallel performance prediction using lost cycles analysis , 1994, Proceedings of Supercomputing '94.

[2]  Bernd Mohr,et al.  KOJAK - A Tool Set for Automatic Performance Analysis of Parallel Programs , 2003, Euro-Par.

[3]  Hong Linh Truong,et al.  Performance Analysis for MPI Applications with SCALEA , 2002, PVM/MPI.

[4]  Jesús Labarta,et al.  A Framework for Performance Modeling and Prediction , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[5]  Jerry C. Yan,et al.  Normalized performance indices for message passing parallel programs , 1994, ICS '94.

[6]  Jeffrey D. Smith,et al.  Design and Analysis of Algorithms , 2009, Lecture Notes in Computer Science.

[7]  Brian K. Schmidt,et al.  Empirical analysis of overheads in cluster environments , 1994, Concurr. Pract. Exp..

[8]  J. Mark Bull,et al.  A hierarchical classification of overheads in parallel programs , 1996, Software Engineering for Parallel and Distributed Systems.

[9]  Josva Kleist,et al.  Migration = cloning; aliasing , 1999 .

[10]  service Topic collections Notes , .

[11]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[12]  George Karypis,et al.  Introduction to Parallel Computing , 1994 .

[13]  D.A. Reed,et al.  Scalable performance analysis: the Pablo performance analysis environment , 1993, Proceedings of Scalable Parallel Libraries Conference.

[14]  Jack J. Dongarra,et al.  A Portable Programming Interface for Performance Evaluation on Modern Processors , 2000, Int. J. High Perform. Comput. Appl..

[15]  Cherri M. Pancake,et al.  Applying Human Factors to the Design of Performance Tools , 1999, Euro-Par.

[16]  Pankaj Mehra,et al.  Performance measurement, visualization and modeling of parallel and distributed programs using the AIMS toolkit , 1995, Softw. Pract. Exp..