Prophesy: an infrastructure for analyzing and modeling the performance of parallel and distributed applications

Taylor, Valerie; Wu, Xingfu; Geisler, Jonathan; Li, X.; Lan, Zhiling; Hereld, Mark; Judson, I. R.

doi:10.1109/hpdc.2000.868668

Cited by 48 publications

(44 citation statements)

References 2 publications

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“…Our approach, instead, combines benchmarking results from multiple platforms for cross-platform prediction. In addition, work on cross-platform performance prediction (e.g., Prophesy [33] or convolution methods to map hand-coded kernels to machine profiles [4]) was often based on modeling computational kernels instead of complex applications with diverse tasks.…”

Section: Related Workmentioning

confidence: 99%

Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution

Yang

Mueller

ACM/IEEE SC 2005 Conference (SC'05)

114

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Performance prediction across platforms is increasingly important as developers can choose from a wide range of execution platforms. The main challenge remains to perform accurate predictions at a low-cost across different architectures.In this paper, we derive an affordable method approaching cross-platform performance translation based on relative performance between two platforms. We argue that relative performance can be observed without running a parallel application in full. We show that it suffices to observe very short partial executions of an application since most parallel codes are iterative and behave predictably manner after a minimal startup period. This novel prediction approach is observation-based. It does not require program modeling, code analysis, or architectural simulation. Our performance results using real platforms and production codes demonstrate that prediction derived from partial executions can yield high accuracy at a low cost. We also assess the limitations of our model and identify future research directions on observationbased performance prediction.

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Section: Related Workmentioning

confidence: 99%

Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution

Yang

Mueller

ACM/IEEE SC 2005 Conference (SC'05)

114

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“…in SCALEA [13] and Prophesy [11]. However, these approaches are tool-specific rather than widelyaccepted data representations.…”

Section: Related Workmentioning

confidence: 99%

“…However, such vocabularies and definitions are not sharable and do not establish a common understanding about the data, thus preventing semantic interoperability between various parties which is an important issue that Grid monitoring and measurement tools have to support. Utilizing relational databases to store performance data [13,11] simplifies sharing of data. However, data models represented in relational database are still very tool-specific and inextensible.…”

Section: Motivationmentioning

confidence: 99%

Performance Analysis, Data Sharing, and Tools Integration in Grids: New Approach Based on Ontology

Truong

Fahringer

2004

Computational Science - ICCS 2004

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Abstract. In this paper, we propose a new approach to performance analysis, data sharing and tools integration in Grids that is based on ontology. We devise a novel ontology for describing the semantics of monitoring and performance data that can be used by performance monitoring and measurement tools. We introduce an architecture for an ontology-based model for performance analysis, data sharing and tools integration. At the core of this architecture is a Grid service which offers facilities for other services to archive and access ontology models along with collected performance data, and to conduct searches and perform reasoning on that data. Using an approach based on ontology, performance data will be easily shared and processed by automated tools, services and human users, thus helping to leverage the data sharing and tools integration, and increasing the degree of automation of performance analysis.

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“…Prophesy [16] is an online database which uploads and stores data from instrumented parallel application runs. Prophesy applies the performance database to manage multi-dimensional performance information for parallel analysis and modeling.…”

Section: Related Workmentioning

confidence: 99%

Parametric Studies in Eclipse with TAU and PerfExplorer

Huck

Spear

Malony

et al. 2009

Euro-Par 2008 Workshops - Parallel Processing

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Abstract. With support for C/C++, Fortran, MPI, OpenMP, and performance tools, the Eclipse integrated development environment (IDE) is a serious contender as a programming environment for parallel applications. There is interest in adding capabilities in Eclipse for conducting workflows where an application is executed under different scenaries and its outputs are processed. For instance, parametric studies are a requirement in many benchmarking and performance tuning efforts, yet there was no experiment management support available for the Eclipse IDE. In this paper, we describe an extension of the Parallel Tools Platform (PTP) plugin for the Eclipse IDE. The extension provides a graphical user interface for selecting experiment parameters, launches build and run jobs, manages the performance data, and launches an analysis application to process the data. We describe our implementation, and discuss three experiment examples which demonstrate the experiment management support.

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Prophesy: an infrastructure for analyzing and modeling the performance of parallel and distributed applications

Cited by 48 publications

References 2 publications

Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution

Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution

Performance Analysis, Data Sharing, and Tools Integration in Grids: New Approach Based on Ontology

Parametric Studies in Eclipse with TAU and PerfExplorer

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