Modeling Master/Worker applications for automatic performance tuning

Eduardo, César; Moreno, Amparo; Sorribes, Joan; Luque, Emilio

doi:10.1016/j.parco.2006.06.005

Cited by 34 publications

(19 citation statements)

References 11 publications

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“…César et al [4] created a dynamic tuning tool for automatic scheduling of workload on different nodes. Their proposed master/worker framework solved the problem of taking optimal tuning decisions for different nodes during the program runtime.…”

Section: Related Workmentioning

confidence: 99%

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The READEX formalism for automatic tuning for energy efficiency

et al. 2017

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Energy efficiency is an important aspect of future exascale systems, mainly due to rising energy cost. Although High performance computing (HPC) applications are compute centric, they still exhibit varying computational characteristics in different regions of the program, such as compute-, memory-, and I/O-bound code regions. Some of today's clusters already offer mechanisms to adjust the system to the resource requirements of an application, e.g., by controlling the CPU frequency. However, manually tuning for improved energy efficiency is a tedious and painstaking task that is often neglected by application developers. The European Union's Horizon 2020 project READEX (Runtime Exploitation of Application Dynamism for Energyefficient eXascale computing) aims at developing a tools-aided approach for improved energy efficiency of current and future HPC applications. To reach this goal, the READEX project combines technologies from two ends of the compute spectrum, embedded systems and HPC, constituting a split design-time/runtime methodology. dynamic auto-tuning of fine-grained application regions using the systems scenario methodology, which was originally developed for improving the energy efficiency in embedded systems. This paper introduces the concepts of the READEX project, its envisioned implementation, and preliminary results that demonstrate the feasibility of this approach.

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Section: Related Workmentioning

confidence: 99%

“…The READEX methodology and its definitions are presented in Sect. 4. A description of the tool suite is provided in Sect.…”

Section: Introductionmentioning

confidence: 99%

The READEX formalism for automatic tuning for energy efficiency

et al. 2017

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“…While a small number of dynamic auto-tuning methodologies and tools exist for run-time optimizations [8], [9], no single standalone dynamic auto-tuning framework currently exists with the capability to target the full breadth of largescale HPC applications being used in academia and industry both now and on the road to Exascale.…”

Section: Related Workmentioning

confidence: 99%

READEX: Linking two ends of the computing continuum to improve energy-efficiency in dynamic applications

Kjeldsberg

Gocht

Gerndt

et al. 2017

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2017

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Abstract-In both the embedded systems and High Performance Computing domains, energy-efficiency has become one of the main design criteria. Efficiently utilizing the resources provided in computing systems ranging from embedded systems to current petascale and future Exascale HPC systems will be a challenging task. Suboptimal designs can potentially cause large amounts of underutilized resources and wasted energy. In both domains, a promising potential for improving efficiency of scalable applications stems from the significant degree of dynamic behaviour, e.g., runtime alternation in application resource requirements and workloads. Manually detecting and leveraging this dynamism to improve performance and energy-efficiency is a tedious task that is commonly neglected by developers. However, using an automatic optimization approach, application dynamism can be analysed at design time and used to optimize system configurations at runtime.The European Union Horizon 2020 READEX (Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing) project will develop a tools-aided auto-tuning methodology inspired by the system scenario methodology used in embedded systems. Dynamic behaviour of HPC applications will be exploited to achieve improved energy-efficiency and performance. Driven by a consortium of European experts from academia, HPC resource providers, and industry, the READEX project aims at developing the first of its kind generic framework to split design time and runtime automatic tuning while targeting heterogeneous system at the Exascale level. This paper describes plans for the project as well as early results achieved during its first year. Furthermore, it is shown how project results will be brought back into the embedded systems domain.

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“…10,11 To define a performance model for dynamic tuning, we have adopted the terminology used by the dynamic tuning tool MATE 3 :…”

Section: Knowledge For Hierarchical Dynamic Tuningmentioning

confidence: 99%

Evaluating a formal methodology for dynamic tuning of large‐scale parallel applications

Martínez

Sikora

Eduardo

et al. 2017

Concurrency and Computation

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SummaryLarge-scale parallel applications performance is usually far from the expected. Dynamic tuning is a powerful technique that helps to improve the performance of parallel applications. To bring this technique to large-scale computers, this work presents a model that enables decentralized dynamic tuning of large-scale parallel applications. In this model, applications are decomposed into disjoint subsets of tasks that can be tuned individually but also abstracted to obtain a global view of the parallel application. The proposed model has been designed as a hierarchical tuning network of distributed analysis modules and implemented in the form of ELASTIC, an environment for large-scale dynamic tuning. Using ELASTIC an experimental evaluation has been conducted over a synthetic large-scale parallel application and a real agent-based parallel application. The results show that the proposed model, embodied in ELASTIC, is able to scale to meet the demands of dynamic tuning over thousands of processes, while effectively improving the performance of large-scale applications. KEYWORDSdynamic tuning, performance analysis, performance tools, scalability, tuning network INTRODUCTIONParallel applications running on supercomputers are able to execute complex scientific parallel applications in a reasonable amount of time. Unfortunately, it is common that the performance expected of these large-scale parallel applications is not easily achieved. Several performance analysis tools, such as Scalasca 1 or TAU, 2 are able to assist developers in identifying the performance problems of these applications in large-scale contexts.However, most of these analysis tools are less useful when applications have execution behaviors that change depending on the input data set or according to data evolution.In this context, performance analysis tools based on automatic and dynamic tuning are necessary. In this approach the three phases of the performance improvement process (monitoring, analysis, and tuning) are performed automatically and continuously while the parallel application is running. However, dynamic tuning of parallel applications is a challenge in a large-scale context. Currently, the tools that offer dynamic tuning 3-6 follow a centralized scheme where a single module is responsible for the global tool control and the analysis and tuning process over the entire parallel application. When working with large-scale parallel applications, a scalability barrier arises from this centralized operation due to the large number of communication connections and the increasing complexity of conducting a holistic performance analysis and tuning.To address the challenge of tuning large-scale parallel applications at runtime, we have defined and designed a model that enables decentralized large-scale dynamic tuning. This is based on decomposing the parallel application in disjoint subsets of tasks that will be analyzed and tuned independently. In addition, an abstraction mechanism is applied on each of these subsets in order to build a sm...

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Modeling Master/Worker applications for automatic performance tuning

Cited by 34 publications

References 11 publications

The READEX formalism for automatic tuning for energy efficiency

The READEX formalism for automatic tuning for energy efficiency

READEX: Linking two ends of the computing continuum to improve energy-efficiency in dynamic applications

Evaluating a formal methodology for dynamic tuning of large‐scale parallel applications

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