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21st International Conference on Parallel and Distributed Computing (Euro-Par)

作者： Hadjidoukas, Panagiotis E. Angelikopoulos, Panagiotis Kulakova, Lina Papadimitriou, Costas Koumoutsakos, Petros Swiss Fed Inst Technol Computat Sci & Engn Lab Zurich Switzerland Univ Thessaly Dept Mech Engn Volos Greece

ISBN: (纸本)9783662480960;9783662480953

We introduce a task-parallel framework for non-intrusive Bayesian Uncertainty Quantification and Propagation of complex and computationally demanding physical models on massively parallel computing architectures. The framework incorporates Laplace asymptotic approximations and stochastic algorithms along with distributed numerical differentiation. Sampling is based on the Transitional Markov Chain Monte Carlo algorithm and its variants while the optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy. Exploitation of task-based parallelism is based on a platform-agnostic adaptive load balancing library that orchestrates scheduling of multiple physical model evaluations on computing platforms that range from multicore systems to hybrid GPU clusters. Experimental results using representative applications demonstrate the flexibility and excellent scalability of the proposed framework.

关键词： task-based parallelism Bayesian uncertainty quantification

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HOMPI: A Hybrid Programming Framework for Expressing and Deploying task-based parallelism

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17th International Euro-Par Conference on Parallel Processing

作者： Dimakopoulos, Vassilios V. Hadjidoukas, Panagiotis E. Univ Ioannina Dept Comp Sci GR-45110 Ioannina Greece

ISBN: (纸本)9783642233975

This paper presents HOMPI, a framework for programming and executing task-based parallel applications on clusters of multiprocessors and multi-cores, while providing interoperability with existing programming systems such as MPI and OpenMP. HOMPI facilitates expressing irregular and adaptive master-worker and divide-and-conquer applications avoiding explicit MPI calls. It also allows hybrid shared-memory / message-passing programming, exploiting fully the availability of multiprocessor and multi-core nodes, as it integrates by design with OpenMP;the runtime infrastructure presents a unified substrate that handles local threads and remote tasks seamlessly, allowing both programming flexibility and increased performance opportunities.

关键词： cluster programming task-based parallelism load balancing MPI

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Pygion: Flexible, Scalable task-based parallelism with Python

Pygion: Flexible, Scalable Task-Based Parallelism with Pytho...

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IEEE/ACM Parallel Applications Workshop / Alternatives To MPI+X Workshop (PAW-ATM)

作者： Slaughter, Elliott Aiken, Alex SLAC Natl Accelerator Lab Menlo Pk CA 94025 USA Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781728159799

Dynamic languages provide the flexibility needed to implement expressive support for task-based parallel programming constructs. We present Pygion, a Python interface for the Legion task-based programming system, and show that it can provide features comparable to Regent, a statically typed programming language with dedicated support for the Legion programming model. Furthermore, we show that the dynamic nature of Python permits the implementation of several key optimizations (index launches, futures, mapping) currently implemented in the Regent compiler. Together these features enable Pygion code that is comparable in expressiveness but more flexible than Regent, and substantially more concise, less error prone, and easier to use than C++ Legion code. Pygion is designed to interoperate with Regent and can use Regent to generate high-performance CPU and GPU kernel implementations. We show that, in combination with high-performance kernels written in Regent, Pygion is able to achieve efficient, scalable execution on up to 512 nodes of the heterogeneous supercomputer Piz Daint.

关键词： task-based parallelism Pygion Legion Python

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EFFICIENT AND SCALABLE ALGORITHMS FOR SMOOTHED PARTICLE HYDRODYNAMICS ON HYBRID SHARED/DISTRIBUTED-MEMORY ARCHITECTURES

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2015年第1期37卷 C95-C121页

作者： Gonnet, Pedro Univ Durham Sch Engn & Comp Sci Durham England Google Switzerland CH-8002 Zurich Switzerland

This paper describes a new fast and implicitly parallel approach to neighbor-finding in multiresolution smoothed particle hydrodynamics (SPH) simulations. This new approach is based on hierarchical cell decompositions and sorted interactions, within a task-based formulation. It is shown to be faster than traditional tree-based codes and to scale better than domain decomposition-based approaches on hybrid shared/distributed-memory parallel architectures, e.g., clusters of multicores, achieving a 40x speedup over the Gadget-2 simulation code.

关键词： smoothed particle hydrodynamics simulation task-based parallelism multicores

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Parallelization of stochastic bounds for Markov chains on multicore and manycore platforms

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JOURNAL OF SUPERCOMPUTING 2018年第4期74卷 1497-1509页

作者： Bylina, Jaroslaw Marie Curie Sklodowska Univ Inst Math Pl M Curie Skodowskiej 5 Lublin Poland

The author demonstrates the methodology for parallelizing of finding stochastic bounds for Markov chains on multicore and manycore platforms. The stochastic bounds algorithm for Markov chains with the sparse matrices is investigated, thus needing a lot of irregular memory access. Its parallel implementations should scale across multiple threads and characterize with a high performance and performance portability between multicore and manycore platforms. The presented methods are built on the usage of two parallelization extensions of the C++ language: OpenMP and Cilk Plus. For this two extensions, we use two programming models, namely loop parallelism and task-based parallelism. The numerical experiments show the execution time of the implementations and the scalability on multicore and manycore platforms. This work provides the parallel implementations and at the same time presents an educational example of how computer science problems with irregular memory access can be implemented for high performance using OpenMP and Cilk Plus.

关键词： Intel Xeon Phi Stochastic bounds of Markov chains Sparse matrices Intel Cilk Plus task-based parallelism For-loop parallelism

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Lightweight task offloading exploiting MPI wait times for parallel adaptive mesh refinement

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第24期32卷 e5916-e5916页

作者： Samfass, Philipp Weinzierl, Tobias Charrier, Dominic E. Bader, Michael Tech Univ Munich Dept Informat Garching Germany Univ Durham Comp Sci Durham England

Balancing the workload of sophisticated simulations is inherently difficult, since we have to balance both computational workload and memory footprint over meshes that can change any time or yield unpredictable cost per mesh entity, while modern supercomputers and their interconnects start to exhibit fluctuating performance. We propose a novel lightweight balancing technique for MPI+X to accompany traditional, prediction-based load balancing. It is a reactive diffusion approach that uses online measurements of MPI idle time to migrate taskstemporarilyfrom overloaded to underemployed ranks. tasks are deployed to ranks which otherwise would wait, processed with high priority, and made available to the overloaded ranks again. This migration is nonpersistent. Our approach hijacks idle time to do meaningful work and is totally nonblocking, asynchronous and distributed without a global data view. Tests with a seismic simulation code developed in the ExaHyPE engine uncover the method's potential. We found speed-ups of up to 2-3 for ill-balanced scenarios without logical modifications of the code base and show that the strategy is capable to react quickly to temporarily changing workload or node performance.

关键词： adaptive mesh refinement MPI plus X reactive load balancing task-based parallelism

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A taxonomy of task-based parallel programming technologies for high-performance computing

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JOURNAL OF SUPERCOMPUTING 2018年第4期74卷 1422-1434页

作者： Thoman, Peter Dichev, Kiril Heller, Thomas Iakymchuk, Roman Aguilar, Xavier Hasanov, Khalid Gschwandtner, Philipp Lemarinier, Pierre Markidis, Stefano Jordan, Herbert Fahringer, Thomas Katrinis, Kostas Laure, Erwin Nikolopoulos, Dimitrios S. Univ Innsbruck A-6020 Innsbruck Austria Queens Univ Belfast Belfast BT7 1NN Antrim North Ireland Univ Erlangen Nurnberg D-91058 Erlangen Germany KTH Royal Inst Technol S-10044 Stockholm Sweden IBM Ireland Dublin 15 Ireland

task-based programming models for shared memory-such as Cilk Plus and OpenMP 3-are well established and documented. However, with the increase in parallel, many-core, and heterogeneous systems, a number of research-driven projects have developed more diversified task-based support, employing various programming and runtime features. Unfortunately, despite the fact that dozens of different task-based systems exist today and are actively used for parallel and high-performance computing (HPC), no comprehensive overview or classification of task-based technologies for HPC exists. In this paper, we provide an initial task-focused taxonomy for HPC technologies, which covers both programming interfaces and runtime mechanisms. We demonstrate the usefulness of our taxonomy by classifying state-of-the-art task-based environments in use today.

关键词： High-performance computing task-based parallelism Taxonomy API Runtime system Scheduler Monitoring framework Fault tolerance

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SpECTRE: A task-based discontinuous Galerkin code for relativistic astrophysics

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JOURNAL OF COMPUTATIONAL PHYSICS 2017年 335卷 84-114页

作者： Kidder, Lawrence E. Field, Scott E. Foucart, Francois Schnetter, Erik Teukolsky, Saul A. Bohn, Andy Deppe, Nils Diener, Peter Hebert, Francois Lippuner, Jonas Miller, Jonah Ott, Christian D. Scheel, Mark A. Vincent, Trevor Cornell Univ Cornell Ctr Astrophys & Planetary Sci Ithaca NY 14853 USA Univ Massachusetts Dartmouth Dept Math Dartmouth MA 02747 USA Lawrence Berkeley Natl Lab 1 Cyclotron Rd Berkeley CA 94720 USA Perimeter Inst Theoret Phys Waterloo ON N2L 2Y5 Canada Univ Guelph Dept Phys Guelph ON N1G 2W1 Canada Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Louisiana State Univ Dept Phys & Astron Baton Rouge LA 70803 USA CALTECH Walter Burke Inst Theoret Phys TAPIR Mailcode 350-17 Pasadena CA 91125 USA Univ Toronto Canadian Inst Theoret Astrophys Toronto ON M5S 3H8 Canada Univ Toronto Dept Phys 60 St George St Toronto ON M5S 3H8 Canada

We introduce a new relativistic astrophysics code, SpECTRE, that combines a discontinuous Galerkin method with a task-based parallelism model. SpECTRE's goal is to achieve more accurate solutions for challenging relativistic astrophysics problems such as core-collapse supernovae and binary neutron star mergers. The robustness of the discontinuous Galerkin method allows for the use of high-resolution shock capturing methods in regions where (relativistic) shocks are found, while exploiting high-order accuracy in smooth regions. A task-based parallelism model allows efficient use of the largest supercomputers for problems with a heterogeneous workload over disparate spatial and temporal scales. We argue that the locality and algorithmic structure of discontinuous Galerkin methods will exhibit good scalability within a task-based parallelism framework. We demonstrate the code on a wide variety of challenging benchmark problems in (non)-relativistic (magneto)-hydrodynamics. We demonstrate the code's scalability including its strong scaling on the NCSA Blue Waters supercomputer up to the machine's full capacity of 22,380 nodes using 671,400 threads. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Discontinuous Galerkin Hydrodynamics Magnetohydrodynamics task-based parallelism

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Design and analysis of a task-based parallelization over a runtime system of an explicit finite-volume CFD code with adaptive time stepping

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JOURNAL OF COMPUTATIONAL SCIENCE 2018年 28卷 439-454页

作者： Carpaye, Jean Marie Couteyen Roman, Jean Brenner, Pierre Airbus Grp Innovat 12 Rue Pasteur F-92150 Suresnes France Airbus Safran Launchers 51-61 Route Verneuil F-78130 Les Mureaux France CNRS INRIA Bordeaux INP LaBRI UMR 5800 F-33400 Talence France

FLUSEPA (registered trademark in France No. 134009261) is an advanced simulation tool which performs a large panel of aerodynamic studies. It is the unstructured finite-volume solver developed by Airbus Safran Launchers company to calculate compressible, multidimensional, unsteady, viscous and reactive flows around bodies in relative motion. The time integration in FLUSEPA is done using an explicit temporal adaptive method. The current production version of the code is based on MPI and OpenMP. This implementation leads to important synchronizations that must be reduced. To tackle this problem, we present the study of a task-based parallelization of the aerodynamic solver of FLUSEPA using the runtime system StarPU and combining up to three levels of parallelism. We validate our solution by the simulation (using a finite-volume mesh with 80 million cells) of a take-off blast wave propagation for Ariane 5 launcher. (C) 2017 Elsevier B.V. All rights reserved.

关键词： High performance computing Computational fluid dynamics Aerospace industry task-based parallelism Runtime system

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Performance Comparison of HPX Versus Traditional Parallelization Strategies for the Discontinuous Galerkin Method

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JOURNAL OF SCIENTIFIC COMPUTING 2019年第2期80卷 878-902页

作者： Bremer, Maximilian Kazhyken, Kazbek Kaiser, Hartmut Michoski, Craig Dawson, Clint Univ Texas Austin Oden Inst Computat Engn & Sci Austin TX 78712 USA Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA

As high performance computing moves towards the exascale computing regime, applications are required to expose increasingly fine grain parallelism to efficiently use next generation supercomputers. Intended as a solution to the programming challenges associated with these architectures, High Performance ParalleX (HPX) is a task-based C++ runtime, which emphasizes the use of lightweight threads and algorithm-dependent synchronization to maximize parallelism exposed by the application to the machine. The aim of this work is to explore the performance benefits of an HPX parallelization versus a MPI parallelization for the discontinuous Galerkin finite element method for the two-dimensional shallow water equations. We present strong and weak scaling results comparing the performance of HPX versus a MPI parallelization strategy on Knights Landing architectures. Our results indicate that for average task sizes of 3.6 ms, HPX's runtime overhead is offset by more efficient execution of the application. Furthermore, we demonstrate that running with sufficiently large task granularity, HPX is able to outperform the MPI parallelization by a factor of approximately 1.2 for up to 128 nodes.

关键词： Parallel computing Discontinuous Galerkin Shallow water equations Manycore computing task-based parallelism Knights Landing

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