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27th International European Conference on Parallel and Distributed Computing (Euro-Par)

作者： Guidotti, Nicolas Ceyrat, Pedro Barreto, Joao Monteiro, Jose Rodrigues, Rodrigo Fonseca, Ricardo Martorell, Xavier Pena, Antonio J. Univ Lisbon Inst Super Tecn INESC ID Lisbon Portugal Univ Lisbon Inst Super Tecn IPFN Lisbon Portugal DCTI ISCTE IUL Lisbon Portugal Barcelona Supercomp Ctr BSC Barcelona Spain

ISBN: (纸本)9783030856656;9783030856649

Recently, task-based programming models have emerged as a prominent alternative among shared-memory parallel programming paradigms. Inherently asynchronous, these models provide native support for dynamic load balancing and incorporate data flow concepts to selectively synchronize the tasks. However, tasking models are yet to be widely adopted by the HPC community and their effective advantages when applied to non-trivial, real-world HPC applications are still not well comprehended. In this paper, we study the parallelization of a production electromagnetic particle-in-cell (EM-PIC) code for kinetic plasma simulations exploring different strategies using asynchronous task-based models. Our fully asynchronous implementation not only significantly outperforms a conventional, synchronous approach but also achieves near perfect scaling for 48 cores.

关键词： Manycore parallelism task-based programming Asynchronous parallelism Particle-in-cell Kinetic plasma simulations

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The Chunks and tasks Matrix Library

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SOFTWAREX 2022年 19卷

作者： Rubensson, Emanuel H. Rudberg, Elias Kruchinina, Anastasia Artemov, Anton G. Uppsala Univ Dept Informat Technol Div Sci Comp Box 337 SE-75105 Uppsala Sweden

We present a C++ header-only parallel sparse matrix library, based on sparse quadtree representation of matrices using the Chunks and tasks programming model. The library implements a number of sparse matrix algorithms for distributed memory parallelization that are able to dynamically exploit data locality to avoid movement of data. This is demonstrated for the example of block-sparse matrix-matrix multiplication applied to three sequences of matrices with different nonzero structure, using the CHT-MPI 2.0 runtime library implementation of the Chunks and tasks model. The runtime library succeeds to dynamically load balance the calculation regardless of the sparsity structure. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： Chunks and tasks programming model Block-sparse Parallelization Quadtree Sparse matrices task-based programming

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Resource allocation for task-level speculative scientific applications: A proof of concept using Parallel Trajectory Splicing

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PARALLEL COMPUTING 2022年 112卷

作者： Garmon, Andrew Ramakrishnaiah, Vinay Perez, Danny Los Alamos Natl Lab Theoret Div Los Alamos NM 87545 USA Los Alamos Natl Lab Comp Computat & Stat Sci Div Los Alamos NM 87545 USA

The constant increase in parallelism available on large-scale distributed computers poses major scalability challenges to many scientific applications. A common strategy to improve scalability is to express algorithms in terms of independent tasks that can be executed concurrently on a runtime system. In this manuscript, we consider a generalization of this approach where task-level speculation is allowed. In this context, a probability is attached to each task which corresponds to the likelihood that the output of the speculative task will be consumed as part of the larger calculation. We consider the problem of optimal resource allocation to each of the possible tasks so as to maximize the total expected computational throughput. The power of this approach is demonstrated by analyzing its application to Parallel Trajectory Splicing, a massively-parallel long-time-dynamics method for atomistic simulations.

关键词： task-based programming Speculation Resource allocation Discrete event simulation Accelerated molecular dynamics

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An asynchronous and task-based implementation of peridynamics utilizing HPX-the C++ standard library for parallelism and concurrency

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SN APPLIED SCIENCES 2020年第12期2卷 1-21页

作者： Diehl, Patrick Jha, Prashant K. Kaiser, Hartmut Lipton, Robert Levesque, Martin Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Univ Texas Austin Oden Inst Computat Engn & Sci Austin TX 78712 USA Louisiana State Univ Dept Math Baton Rouge LA 70803 USA Polytech Montreal Dept Multi Scale Mech Montreal PQ Canada

On modern supercomputers, asynchronous many task systems are emerging to address the new architecture of computational nodes. Through this shift of increasing cores per node, a new programming model with focus on handling of the fine-grain parallelism with increasing amount of cores per computational node is needed. Asynchronous Many task (AMT) run time systems represent a paradigm for addressing the fine-grain parallelism. They handle the increasing amount of threads per node and concurrency. HPX, a open source C++ standard library for parallelism and concurrency, is one AMT which is conforming to the C++ standard. Motivated by the impressive performance of asynchronous task-based parallelism through HPX to N-body problem and astrophysics simulation, in this work, we consider its application to the Peridynamics theory. Peridynamics is a non-local generalization of continuum mechanics tailored to address discontinuous displacement fields arising in fracture mechanics. Peridynamics requires considerable computing resources, owing to its non-local nature of formulation, offering a scope for improved computing performance via asynchronous task-based parallelism. Our results show that HPX-based peridynamic computation is scalable, and the scalability is in agreement with the theory. In addition to the scalability, we also show the validation results and the mesh convergence results. For the validation, we consider implicit time integration and compare the result with the classical continuum mechanics (CCM) (peridynamics under small deformation should give similar results as CCM). For the mesh convergence, we consider explicit time integration and show that the results are in agreement with theoretical claims in previous works.

关键词： Peridynamics finite difference approximation concurrency parallelization HPX task-based programming asynchronous many task systems

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From tasks graphs to asynchronous distributed checkpointing with local restart 10

From tasks graphs to asynchronous distributed checkpointing ...

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IEEE/ACM 10th Workshop on Fault Tolerance for HPC at eXtreme Scale (FTXS)

作者： Lion, Romain Thibault, Samuel Univ Bordeaux Inria Bordeaux Sud Ouest Bordeaux France

ISBN: (纸本)9780738110806

The ever-increasing number of computation units assembled in current HPC platforms leads to a concerning increase in fault probability. Traditional checkpoint/restart strategies avoid wasting large amounts of computation time when such fault occurs. With the increasing amount of data dealt with by current applications, these strategies however suffer from their data transfer demand becoming unreasonable, or the entailed global synchronizations. Meanwhile, the current trend towards task-based programming is an opportunity to revisit the principles of the checkpoint/restart strategies. We here propose a checkpointing scheme which is closely tied to the execution of task graphs. We describe how it allows for completely asynchronous and distributed checkpointing, as well as localized node restart, thus opening up for very large scalability. We also show how a synergy between the application data transfers and the checkpointing transfers can lead to a reasonable additional network load, measured to be lower than +10% on a dense linear algebra example.

关键词： Fault tolerance task-based programming checkpoint-restart buddy in-memory

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Memory performance analysis strategies at runtime level for task-based applications over heterogeneous platforms

Memory performance analysis strategies at runtime level for ...

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作者： Nesi, Lucas Leandro

programming parallel applications for heterogeneous High Performance Computing platforms is easier when using the task-based programming paradigm, where a Direct Acyclic Graph (DAG) of tasks models the application behavior. The simplicity exists because a runtime, like StarPU, takes care of many activities usually carried out by the application developer, such as task scheduling, load balancing, and memory management. This memory management refers to the runtime responsibility for handling memory operations, like copying the necessary data to the location where a given task is scheduled to execute. Poor scheduling or lack of appropriate information may lead to inadequate memory management by the runtime. Discover if an application presents memory-related performance problems is complex. The task-based applications’ and runtimes’ programmers would benefit from specialized performance analysis methodologies that check for possible memory management problems. In this way, this work proposes methods and tools to investigate heterogeneous CPU-GPU-Disk memory management of the StarPU runtime, a popular task-based middleware for HPC applications. The base of these methods is the execution traces that are collected by the runtime. These traces provide information about the runtime decisions and the system performance; however, a simple application can have huge amounts of trace data stored that need to be analyzed and converted to useful metrics or visualizations. The use of a methodology specific to task-based applications could lead to a better understanding of memory behavior and possible performance optimizations. The proposed strategies are applied on three different problems, a dense Cholesky solver, a CFD simulation, and a sparse QR factorization. On the dense Cholesky solver, the strategies found a problem on StarPU that a correction leads to 66% performance improvement. On the CFD simulation, the strategies guided the insertion of extra information on the DAG and d

关键词： Memoria : Computadores Performance analysis Análise de desempenho task-based programming Dissertação

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PLASMA: Parallel Linear Algebra Software for Multicore Using OpenMP

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ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE 2019年第2期45卷 16-16页

作者： Dongarra, Jack Gates, Mark Haidar, Azzam Kurzak, Jakub Luszczek, Piotr Wu, Panruo Yamazaki, Ichitaro Yarkhan, Asim Abalenkovs, Maksims Bagherpour, Negin Hammarling, Sven Sistek, Jakub Stevens, David Zounon, Mawussi Relton, Samuel D. Univ Tennessee Dept Elect Engn & Comp Sci 1122 Volunteer BlvdSuite 203 Knoxville TN 37996 USA Univ Houston Dept Comp Sci 3551 Cullen Blvd Houston TX 77204 USA Univ Manchester Sch Math Manchester M13 9PL Lancs England Czech Acad Sci Inst Math Zitna 25 Prague 11567 Czech Republic Numer Algorithms Grp Manchester One53 Portland St Manchester M1 3LD Lancs England Univ Leeds Inst Hlth Sci Leeds LS2 9LJ W Yorkshire England

The recent version of the Parallel Linear Algebra Software for Multicore Architectures (PLASMA) library is based on tasks with dependencies from the OpenMP standard. The main functionality of the library is presented. Extensive benchmarks are targeted on three recent multicore and manycore architectures, namely, an Intel Xeon, Intel Xeon Phi, and IBM POWER 8 processors.

关键词： Numerical linear algebra libraries tile algorithms task-based programming multicore processors OpenMP PLASMA

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task-based Cholesky Decomposition on Knights Corner Using OpenMP

Task-Based Cholesky Decomposition on Knights Corner Using Op...

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International Supercomputing Conference (ISC High Performance)

作者： Dorris, Joseph Kurzak, Jakub Luszczek, Piotr YarKhan, Asim Dongarra, Jack Innovat Comp Lab Knoxville TN 37996 USA

ISBN: (纸本)9783319460796;9783319460789

The growing popularity of the Intel Xeon Phi coprocessors and the continued development of this new many-core architecture have created the need for an open-source, scalable, and cross-platform taskbased dense linear algebra package that can efficiently use this type of hardware. In this paper, we examined the design modifications necessary when porting PLASMA, a task-based dense linear algebra library, to run effectively on Intel's Knights Corner Xeon Phi coprocessor. First, we modified PLASMA's tiled Cholesky decomposition to use OpenMP for its scheduling mechanism to enable Xeon Phi compatibility. We then compared the performance of our modified code to that of the original dynamic scheduler running on an Intel Xeon Sandy Bridge CPU. Finally, we looked at the performance of the new OpenMP tiled Cholesky decomposition on a Knights Corner coprocessor. We found that desirable performance for this architecture was attainable with the right code optimizations;these changes were necessary to account for differences in the runtimes and in the hardware itself.

关键词： Cholesky decomposition Linear algebra OpenMP PLASMA task-based programming Tile algorithms Xeon Phi

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mCluster: A Software Framework for Portable Device-based Volunteer Computing 16

mCluster: A Software Framework for Portable Device-based Vol...

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16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)

作者： Theodoropoulos, Dimitris Chrysos, Grigorios Koidis, Iosif Charitopoulos, George Pissadakis, Emmanouil Varikos, Antonis Pnevmatikatos, Dionisios Smaragdos, Georgios Strydis, Christos Zervos, Nikos Tech Univ Crete Chania Dept Elect & Comp Engn Microprocessor & Hardware Lab Khania Greece Erasmus MC Dept Neurosci Rotterdam Netherlands Algosystems SA Athens Greece

ISBN: (纸本)9781509024537

Recent market forecasts predict that the portable computing trend will vastly spread, as by 2020 there will be more than 3 billion LTE device users worldwide. Motivated by this fact, many companies and research institutes have already launched research projects that utilize portable devices, voluntarily provided by users, to perform the required computations. Many such projects employ Berkeley's BOINC middleware, since it can support a large variety of stationary and mobile devices. However, currently available BOINC high-level APIs, either do not support portable devices or lack advanced processing capabilities (such as inter-node task dependencies) and/or easiness of use. To resolve these issues, we propose the mCluster software framework for application execution powered by the BOINC middleware on portable devices. mCluster adopts a task-based programming model that requires simple, pragma-based annotations of the application software, in order to dynamically resolve task dependencies. To evaluate our framework, we have have mapped a scientific application from the neuroscience domain on an small-scaled network of portable devices. mCluster significantly reduces the required programming effort and complexity to efficiently map BOINC-powered applications with task dependencies on portable devices compared to previous approaches.

关键词： task-based programming internet of things volunteer computing

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Performance Analysis and Optimisation of Two-Sided Factorization Algorithms for Heterogeneous Platform

Performance Analysis and Optimisation of Two-Sided Factoriza...

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15th Annual International Conference on Computational Science (ICCS)

作者： Kabir, Khairul Haidar, Azzam Tomov, Stanimire Dongarra, Jack Univ Tennessee Knoxville TN USA Oak Ridge Natl Lab Oak Ridge TN USA Univ Manchester Manchester Lancs England

Many applications, ranging from big data analytics to nanostructure designs, require the solution of large dense singular value decomposition (SVD) or eigenvalue problems. A first step in the solution methodology for these problems is the reduction of the matrix at hand to condensed form by two-sided orthogonal transformations. This step is standardly used to significantly accelerate the solution process. We present a performance analysis of the main two-sided factorizations used in these reductions: the bidiagonalization, tridiagonalization, and the upper Hessenberg factorizations on heterogeneous systems of multicore CPUs and Xeon Phi coprocessors. We derive a performance model and use it to guide the analysis and to evaluate performance. We develop optimized implementations for these methods that get up to 80% of the optimal performance bounds. Finally, we describe the heterogeneous multicore and coprocessor development considerations and the techniques that enable us to achieve these high-performance results. The work here presents the first highly optimized implementation of these main factorizations for Xeon Phi coprocessors. Compared to the LAPACK versions optmized by Intel for Xeon Phi (in MKL), we achieve up to 50% speedup.

关键词： Eigensolver multicore Xeon Phi task-based programming

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