MATE: Monitoring, Analysis and Tuning Environment for parallel/distributed applications

Morajko, Anna; Caymes-Scutari, Paola; Margalef, Tomás; Luque, Emilio

doi:10.1002/cpe.1126

Cited by 19 publications

(9 citation statements)

References 15 publications

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“…This ESD signal can be easily passed to a third party to investigate the execution behavior. This way is different from placing sensors into application code or using event tracing to gain specific execution information [10], [11], [12], which is more heavyweight.…”

Section: Methodology a Execution Satisfaction Degree (Esd)mentioning

confidence: 99%

On-line automatic resource selection in distributed computing

Líu

Sorensen

Nazir

2009

2009 IEEE International Conference on Cluster Computing and Workshops

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A key problem in executing performance critical applications on distributed computing environments (e.g. the Grid) is the resource selection. Research related to "automatic resource selection" aims to select suitable resources on behalf of users to optimize the execution performance. However, most of current approaches are based on the static principle (i.e. resource selection is performed prior to execution) and need detailed application-specific information. In the paper, we introduce a novel on-line automatic resource selection approach. This approach is based on a simple control theory: the application continuously reports the Execution Satisfaction Degree (ESD) to the middleware Application Agent (AA), which relies on the reported ESD values to learn the execution behavior and tune the execution environment by adding/replacing/deleting resources at runtime in order to satisfy users' performance requirements. We use a utility-based learning and tuning algorithm to enable the automatic resource tuning/selection. A typical 2-D heat equation application is used to validate the approach. Results show that without resource or application knowledge being provided in advance, the approach is able to find the best-effort resources to satisfy users' execution requirements, and classify resources according to their contribution made to the application.

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Section: Methodology a Execution Satisfaction Degree (Esd)mentioning

confidence: 99%

On-line automatic resource selection in distributed computing

Líu

Sorensen

Nazir

2009

2009 IEEE International Conference on Cluster Computing and Workshops

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“…MATE [14] performs dynamic tuning in three basic and continuous phases: monitoring, performance analysis and application modification. This environment dynamically and automatically instruments a running application to gather information about the application's behavior.…”

Section: Related Workmentioning

confidence: 99%

Online Adaptive Code Generation and Tuning

Tiwari

Hollingsworth

2011

2011 IEEE International Parallel &Amp; Distributed Processing Symposium

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Abstract-In this paper, we present a runtime compilation and tuning framework for parallel programs. We extend our prior work on our auto-tuner, Active Harmony, for tunable parameters that require code generation (for example, different unroll factors). For such parameters, our auto-tuner generates and compiles new code on-the-fly. Effectively, we merge traditional feedback directed optimization and just-in-time compilation. We show that our system can leverage available parallelism in today's HPC platforms by evaluating different code-variants on different nodes simultaneously. We evaluate our system on two parallel applications and show that our system can improve runtime execution by up to 46% compared to the original version of the program.

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“…MATE [57] performs dynamic tuning in three basic and continuous phases: monitoring, performance analysis and application modification. This environment dynamically and automatically instruments a running application to gather information about the application's behavior.…”

Section: Runtime Auto-tunersmentioning

confidence: 99%

Tuning parallel applications in parallel

2009

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Auto-tuning has recently received significant attention from the High Performance Computing community. Most auto-tuning approaches are specialized to work either on specific domains such as dense linear algebra and stencil computations, or only at certain stages of program execution such as compile time and runtime.Real scientific applications, however, demand a cohesive environment that can efficiently provide auto-tuning solutions at all stages of application development and deployment. Towards that end, we describe a unified end-to-end approach to autotuning scientific applications. Our system, Active Harmony, takes a search-based collaborative approach to auto-tuning. Application programmers, library writers and compilers collaborate to describe and export a set of performance related tunable parameters to the Active Harmony system. These parameters define a tun- Active Harmony supports runtime adaptive code-generation and tuning for parameters that require new code (e.g. unroll factors). Effectively, we merge traditional feedback directed optimization and just-in-time compilation. This feature also enables application developers to write applications once and have the autotuner adjust the application behavior automatically when run on new systems. We evaluated our system on multiple large-scale parallel applications and showed that our system can improve the execution time by up to 46% compared to the original version of the program.Finally, we believe that the success of any auto-tuning research depends on how effectively application developers, domain-experts and auto-tuners communicate and work together. To that end, we have developed and released a simple and extensible language that standardizes the parameter space representation. Using this language, developers and researchers can collaborate to export tunable parameters to the tuning frameworks. Relationships (e.g. ordering, dependencies, constraints, ranking) between tunable parameters and search-hints can also be expressed.

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MATE: Monitoring, Analysis and Tuning Environment for parallel/distributed applications

Cited by 19 publications

References 15 publications

On-line automatic resource selection in distributed computing

On-line automatic resource selection in distributed computing

Online Adaptive Code Generation and Tuning

Tuning parallel applications in parallel

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