Recent developments and case studies in performance visualization using ParaGraph
Proceedings of the workshop on performance measurement and visualization on Performance measurement and visualization of parallel systems
Analytical performance prediction on multicomputers
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
SIGMETRICS '94 Proceedings of the 1994 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Analysis and optimization of software pipeline performance on MIMD parallel computers
Journal of Parallel and Distributed Computing
Parallel performance prediction using lost cycles analysis
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Automated Scalability Analysis of Message-Passing Parallel Programs
IEEE Parallel & Distributed Technology: Systems & Technology
Sigma II: A Tool Kit for Building Parallelizing Compilers and Performance Analysis Systems
Proceedings of the IFIP WG 10.3 Workshop on Programming Environments for Parallel Computing
Automated Modeling of Message-Passing Programs
MASCOTS '94 Proceedings of the Second International Workshop on Modeling, Analysis, and Simulation On Computer and Telecommunication Systems
Scalability Analysis Tools for SPMD Message-Passing Parallel Programs
MASCOTS '94 Proceedings of the Second International Workshop on Modeling, Analysis, and Simulation On Computer and Telecommunication Systems
Automatic performance prediction to support cross development of parallel programs
SPDT '96 Proceedings of the SIGMETRICS symposium on Parallel and distributed tools
Automated Scalability Analysis of Message-Passing Parallel Programs
IEEE Parallel & Distributed Technology: Systems & Technology
Using Information from Prior Runs to Improve Automated Tuning Systems
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Performance modeling of parallel applications for grid scheduling
Journal of Parallel and Distributed Computing
PerWiz: a what-if prediction tool for tuning message passing programs
VECPAR'04 Proceedings of the 6th international conference on High Performance Computing for Computational Science
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The increasing use of massively parallel supercomputers to solve large-scale scientific problems has generated a need for tools that can predict scalability trends of applications written for these machines. Much work has been done to create simple models that represent important characteristics of parallel programs, such as latency, network contention, and communication volume. But many of these methods still require substantial manual effort to represent an application in the model's format. The MK toolkit described in this paper is the result of an on-going effort to automate the formation of analytic expressions of program execution time, with a minimum of programmer assistance. In this paper we demonstrate the feasibility of our approach, by extending previous work to detect and model communication patterns automatically, with and without overlapped computations. The predictions derived from these models agree, within reasonable limits, with execution times of programs measured on the Intel iPSC/860 and Paragon. Further, we demonstrate the use of MK in selecting optimal computational grain size and studying various scalability metrics.