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检索条件"主题词=Parallel and distributed programming paradigms"

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distributed and parallel programming paradigms on the K computer and a Cluster 2019

Distributed and Parallel Programming Paradigms on the K comp...

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International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia)

作者： Gurhem, Jerome Tsuji, Miwako Petiton, Serge G. Sato, Mitsuhisa Univ Lille CRISTAL Lille France Univ Lille Maison Simulat CNRS Lille France RIKEN AICS Kobe Hyogo Japan

ISBN: (纸本)9781450366328

In this paper, we focus on a distributed and parallel programming paradigm for massively multi-core supercomputers. We introduce YML, a development and execution environment for parallel and distributed applications based on a graph of task components scheduled at runtime and optimized for several middlewares. Then we show why YML may be well adapted to applications running on a lot of cores. The tasks are developed with the PGAS language XMP based on directives. We use YML/XMP to implement the block-wise Gaussian elimination to solve linear systems. We also implemented it with XMP and MPI without blocks. ScaLAPACK was also used to created an non-block implementation of the resolution of a dense linear system through LU factorization. Furthermore, we run it with different amount of blocks and number of processes per task. We find out that a good compromise between the number of blocks and the number of processes per task gives interesting results. YML/XMP obtains results faster than XMP on the K computer and close to XMP, MPI and ScaLAPACK on clusters of CPUs. We conclude that parallel and distributed multi-level programming paradigms like YML/XMP may be interesting solutions for extreme scale computing.

关键词： parallel and distributed programming paradigms Graph of task components Resolution of linear system

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A Current Task-Based programming paradigms Analysis 20th

A Current Task-Based Programming Paradigms Analysis

引用

20th Annual International Conference on Computational Science (ICCS)

作者： Gurhem, Jerome Petiton, Serge G. Univ Lille CRIStAL Ctr Rech Informat Signal & Automat Lille CNRS UMR 9189 F-59000 Lille France Univ Paris Saclay UVSQ CNRS CEAMaison Simulat F-91191 Gif Sur Yvette France

ISBN: (纸本)9783030867973;9783030504250

Task-based paradigm models can be an alternative to MPI. The user defines atomic tasks with a defined input and output with the dependencies between them. Then, the runtime can schedule the tasks and data migrations efficiently over all the available cores while reducing the waiting time between tasks. This paper focus on comparing several task-based programming models between themselves using the LU factorization as benchmark. HPX, PaRSEC, Legion and YML+XMP are task-based programming models which schedule data movement and computational tasks on distributed resources allocated to the application. YML+XMP supports parallel and distributed tasks with XscalableMP, a PGAS language. We compared their performances and scalability are compared to ScaLAPACK, an highly optimized library which uses MPI to perform communications between the processes on up to 64 nodes. We performed a block-based LU factorization with the task-based programming model on up to a matrix of size 49512 x 49512. HPX is performing better than PaRSEC, Legion and YML+XMP but not better than ScaLAPACK. YML+XMP has a better scalability than HPX, Legion and PaRSEC. Regent has trouble scaling from 32 nodes to 64 nodes with our algorithm.

关键词： parallel and distributed programming paradigms Task-based programming models Supercomputers

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