SCALEA: a performance analysis tool for parallel programs

Truong, Hong Linh; Fahringer, Thomas

doi:10.1002/cpe.778

Cited by 25 publications

(19 citation statements)

References 20 publications

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“…Two common forms of measurement include tracing and profiling.A plethora of trace based performance tools are available in both research and industry. Examples of tracing tools include TAU [49], SCALEA [60], KOJAK [66], Vampirtrace [43], and the Sun Analyzer [27]. Tracing can potentially incur large overheads and generate very large trace files.…”

Section: Performance Monitoring and Analysis Toolsmentioning

confidence: 99%

A component infrastructure for performance and power modeling of parallel scientific applications

Bui

Norris

Huck

et al. 2008

Proceedings of the 2008 compFrame/HPC-GECO Workshop on Component Based High Performance

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Characterizing the performance of scientific applications is essential for effective code optimization, both by compilers and by high-level adaptive numerical algorithms. While maximizing power efficiency is becoming increasingly important in current high-performance architectures, there is little or no hardware or software support for detailed power measurements. Hardware counter-based power models are a promising method for guiding software-based techniques for reducing power. We present a component-based infrastructure for performance and power modeling of parallel scientific applications. The power model leverages on-chip performance hardware counters and is designed to model power consumption for modern multiprocessor/multicore systems. Our tool infrastructure includes application components as well as performance/power measurement and analysis components. We collect performance data using the TAU performance component and apply the power model in the performance and power analysis of a PETSc-based parallel fluid dynamics application by using the PerfExplorer component.

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Section: Performance Monitoring and Analysis Toolsmentioning

confidence: 99%

A component infrastructure for performance and power modeling of parallel scientific applications

Bui

Norris

Huck

et al. 2008

Proceedings of the 2008 compFrame/HPC-GECO Workshop on Component Based High Performance

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“…Overhead metrics are based on a classification of temporal overhead for parallel programs [30,31]. Examples of overhead metrics are control of parallelism (denoted by OCTRP), loss of parallelism, etc.…”

Section: Metrics At Code Region Levelmentioning

confidence: 99%

“…Each InvokedApplication is associated with a SIR (Standardized Intermediate Representation) [36], which represents the structure of the application, including main elements of interest for performance monitoring and analysis, in XML, with a DRG (Dynamic Coderegion Callgraph), which represents the dynamic code region call graph [30], and with events occurred inside the application.…”

Section: Ontology For Performance Data Of Workflowsmentioning

confidence: 99%

“…Performance metrics in [44,45] are associated with activity level. Not all the metrics mentioned in this paper are new; various metrics have been presented in previous work and existing parallel tools, e.g., in [44,45,32,30,49] but they are targeted to a single (parallel) application or to workflows but at the activity level only. Until now there is no ontology that collects such a large number of metrics.…”

Section: Related Workmentioning

confidence: 99%

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Performance metrics and ontologies for Grid workflows

Truong

Dustdar

Fahringer

2007

Future Generation Computer Systems

Self Cite

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Many Grid workflow middleware services require knowledge about the performance behavior of Grid applications/services in order to effectively select, compose, and execute workflows in dynamic and complex Grid systems. To provide performance information for building such knowledge, Grid workflow performance tools have to select, measure, and analyze various performance metrics of workflows. However, there is a lack of a comprehensive study of performance metrics which can be used to evaluate the performance of a workflow executed in the Grid. Moreover, given the complexity of both Grid systems and workflows, semantics of essential performance-related concepts and relationships, and associated performance data in Grid workflows should be well described. In this paper, we analyze performance metrics that performance monitoring and analysis tools should provide during the evaluation of the performance of Grid workflows. Performance metrics are associated with multiple levels of abstraction. We introduce an ontology for describing performance data of Grid workflows and illustrate how the ontology can be utilized for monitoring and analyzing the performance of Grid workflows.

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“…Based on these metrics, various exploratory data analysis techniques can be employed, e.g., load imbalance, metric ratio. We extend our overhead analysis for parallel programs [39] …”

Section: Activity Levelmentioning

confidence: 99%

Dynamic Instrumentation, Performance Monitoring and Analysis of Grid Scientific Workflows

2005

Self Cite

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While existing work concentrates on developing QoS models of business workflows and Web services, few tools have been developed to support the monitoring and performance analysis of scientific workflows in Grids. This paper describes novel Grid services for dynamic instrumentation of Grid-based applications, performance monitoring and analysis of Grid scientific workflows. We describe a Grid dynamic instrumentation service that provides a widely accessible interface for other services and users to conduct the dynamic instrumentation of Grid applications during the runtime. We introduce a Grid performance analysis service for Grid scientific workflows. The analysis service utilizes various types of data including workflow graphs, monitoring data of resources, execution status of activities, and performance measurements obtained from the dynamic instrumentation of invoked applications, and provides a rich set of functionalities and features to support the online monitoring and performance analysis of scientific workflows. Workflows and their relevant information including performance metrics are stored and utilized for comparing the performance of constructs of different workflows and for supporting multi-workflow analysis.

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SCALEA: a performance analysis tool for parallel programs

Cited by 25 publications

References 20 publications

A component infrastructure for performance and power modeling of parallel scientific applications

A component infrastructure for performance and power modeling of parallel scientific applications

Performance metrics and ontologies for Grid workflows

Dynamic Instrumentation, Performance Monitoring and Analysis of Grid Scientific Workflows

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