Optimal Mapping of a Parallel Application Processes onto Heterogeneous Platform

Kalinov, Alexey; Klimov, S.

doi:10.1109/ipdps.2005.310

Cited by 14 publications

(8 citation statements)

References 10 publications

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“…This results in non realistic solutions or solutions that do not fit the real problem. Also, many works deal with the problem of mapping parallel applications on heterogeneous platforms as an assignment problem without explicitly describing the corresponding mathematical model . They only present algorithms for assigning resources without a clear formulation of the problem, which make their results difficult to exploit.…”

Section: Related Workmentioning

confidence: 99%

Task management on fully heterogeneous micro‐server system: Modeling and resolution strategies

Zaourar

Ait

Philippe

2018

Concurrency and Computation

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Summary Many of today's important applications of our everyday lives, eg, weather forecast, design of plane and car shapes, medical analysis, or search engine queries depend on massively parallel computer programs executed in data centers. A large amount of energy is used to power them, and it is of primary importance to compute more efficiently to sustain the increasing demand of computing power while keeping energy consumption reasonable. One promising research path in this domain is heterogeneous systems since specific computing resources (processors, accelerators, etc) are more adapted to efficiently execute parts of applications. Nevertheless, the exploitation of these platforms raises new challenges in terms of application management optimization. The aim of our work is to determine effective algorithms to exploit these heterogeneous platforms by finding appropriate application mapping and scheduling to optimize the execution time and energy consumption with respect to various constraints. To achieve this goal, there is a need of a detailed modeling of the applications and the underlying hardware to be able to find realistic solutions. In this paper, we propose such a model, provide two implementations with state‐of‐the‐art tools, and propose a fast greedy online resolution algorithm and preliminary mapping and scheduling numerical results.

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Section: Related Workmentioning

confidence: 99%

Task management on fully heterogeneous micro‐server system: Modeling and resolution strategies

Zaourar

Ait

Philippe

2018

Concurrency and Computation

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“…A different approach for load balancing is taken in [6]. The problem is broken down into many small processes, each requiring an equal amount of work and data storage.…”

Section: Other Related Workmentioning

confidence: 99%

Column-Based Matrix Partitioning for Parallel Matrix Multiplication on Heterogeneous Processors Based on Functional Performance Models

Clarke

Lastovetsky

Рычков

2012

Euro-Par 2011: Parallel Processing Workshops

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In this paper we present a new data partitioning algorithm to improve the performance of parallel matrix multiplication of dense square matrices on heterogeneous clusters. Existing algorithms either use single speed performance models which are too simplistic or they do not attempt to minimise the total volume of communication. The functional performance model (FPM) is more realistic then single speed models because it integrates many important features of heterogeneous processors such as the processor heterogeneity, the heterogeneity of memory structure, and the effects of paging. To load balance the computations the new algorithm uses FPMs to compute the area of the rectangle that is assigned to each processor. The total volume of communication is then minimised by choosing a shape and ordering so that the sum of the halfperimeters is minimised. Experimental results demonstrate that this new algorithm can reduce the total execution time of parallel matrix multiplication in comparison to existing algorithms.

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“…Then, a derived model is used to estimate the optimal PE configuration and process allocation. Kalinov and Klimov [14] investigate the HeHo strategy where the performance of the processor is given as a function of the number of processes running on the processor and the amount of data distributed to the processor. They present an algorithm that computes optimal number of processes and their distribution over processors minimizing the execution time of the application.…”

Section: Literature Surveymentioning

confidence: 99%

HeteroMPI+ScaLAPACK: Towards a ScaLAPACK (Dense Linear Solvers) on Heterogeneous Networks of Computers

Reddy¹,

Lastovetsky

2006

High Performance Computing - HiPC 2006

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Abstract. The paper presents a tool that ports ScaLAPACK programs designed to run on massively parallel processors to Heterogeneous Networks of Computers. The tool converts ScaLAPACK programs to HeteroMPI programs. The resulting HeteroMPI programs do not aim to extract the maximum performance from a Heterogeneous Networks of Computers but provide an easy and simple way to execute the ScaLAPACK programs on such networks with good performance improvements. We demonstrate the efficiency of the resulting HeteroMPI programs by performing experiments with a matrix multiplication application on a local network of heterogeneous computers.

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Optimal Mapping of a Parallel Application Processes onto Heterogeneous Platform

Cited by 14 publications

References 10 publications

Task management on fully heterogeneous micro‐server system: Modeling and resolution strategies

Task management on fully heterogeneous micro‐server system: Modeling and resolution strategies

Column-Based Matrix Partitioning for Parallel Matrix Multiplication on Heterogeneous Processors Based on Functional Performance Models

HeteroMPI+ScaLAPACK: Towards a ScaLAPACK (Dense Linear Solvers) on Heterogeneous Networks of Computers

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