Parallel performance evaluation: The medea tool

Calzarossa, Maria Carla; Massari, Luisa; Merlo, Alessandro P.; Tessera, Daniele

doi:10.1007/3-540-61142-8_592

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Our work is empirically driven and directly relevant to existing applications. In fact, our analysis focuses on the Message Passing Interface (MPI) [9,20] because it serves as an important foundation for a large group of high performance applications and communication scalability will become an increasingly important problem as the size of computing systems continues to increase [4,18,23]. …”

Section: Introductionmentioning

confidence: 99%

Statistical scalability analysis of communication operations in distributed applications

Vetter

McCracken

2001

SIGPLAN Not.

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Current trends in high performance computing suggest that users will soon have widespread access to clusters of multiprocessors with hundreds, if not thousands, of processors. This unprecedented degree of parallelism will undoubtedly expose scalability limitations in existing applications, where scalability is the ability of a parallel algorithm on a parallel architecture to effectively utilize an increasing number of processors. Users will need precise and automated techniques for detecting the cause of limited scalability. This paper addresses this dilemma. First, we argue that users face numerous challenges in understanding application scalability: managing substantial amounts of experiment data, extracting useful trends from this data, and reconciling performance information with their application's design. Second, we propose a solution to automate this data analysis problem by applying fundamental statistical techniques to scalability experiment data. Finally, we evaluate our operational prototype on several applications, and show that statistical techniques offer an effective strategy for assessing application scalability. In particular, we find that non-parametric correlation of the number of tasks to the ratio of the time for communication operations to overall communication time provides a reliable measure for identifying communication operations that scale poorly.

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Section: Introductionmentioning

confidence: 99%

Statistical scalability analysis of communication operations in distributed applications

Vetter

McCracken

2001

SIGPLAN Not.

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Evaluation of parallel application's behavior in message passing environment

Bubak

Funika

Moscinski

1997

Recent Advances in Parallel Virtual Machine and Message Passing Interface

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Towards Comparative Profiling of Parallel Applications with PPerfDB

Hansen¹

2000

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I have designed and implemented modules which extend the PPerfDB performance tool to allow access to existing performance data generated by several commonly used tracing tools. Access occurs from within the experiment management framework provided by PPerfDB for the identification of system parameters, the representation of multiple sets of execution data, and the formulation of data queries. Furthermore, I have designed and implemented an additional module that will generate new data using dynamic instrumentation under the control of PPerfDB. This was done to enable the creation of novel experiments for performance hypothesis testing and to ultimately automate the diagnostic and tuning process.As data from such diverse sources has very different representations, various techniques to allow comparisons are presented. I have generalized the definition of the Performance Difference operator, which automatically detects divergence in multiple data sets, and I have defined an Overlay operation to provide uniform access to both dynamically generated and tracefile based data. The use and application of these new operations along with an indication of some of the issues involved in the creation of a fully automatic comparative profilier is presented via several case studies performed on an IBM SP2 using different versions of an MPI application.

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Parallel performance evaluation: The medea tool

Cited by 3 publications

References 8 publications

Statistical scalability analysis of communication operations in distributed applications

Statistical scalability analysis of communication operations in distributed applications

Evaluation of parallel application's behavior in message passing environment

Towards Comparative Profiling of Parallel Applications with PPerfDB

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