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Workshop on Python for High-Performance and Scientific Computing (PyHPC)

作者： Clément Foyer Javier Conejero Jorge Ejarque Rosa M. Badia Adrian Tate Simon McIntosh-Smith HPE HPC/AI EMEA Research Lab Bristol United Kingdom High Performance Computing Research Group University of Bristol Bristol United Kingdom Barcelona Supercomputing Center Barcelona Spain Numerical Algorithms Group Ltd. (NAG) Oxford United Kingdom

ISBN: (数字)9780738110868

ISBN: (纸本)9781665422864

Python has been gaining some traction for years in the world of scientific applications. However, the high-level abstraction it provides may not allow the developer to use the machines to their peak performance. To address this, multiple strategies, sometimes complementary, have been developed to enrich the software ecosystem either by relying on additional libraries dedicated to efficient computation (e.g., NumPy) or by providing a framework to better use HPC scale infrastructures (e.g., PyCOMPSs).In this paper, we present a Python extension based on SharedArray that enables the support of system-provided shared memory and its integration into the PyCOMPSs programming model as an example of integration to a complex Python environment. We also evaluate the impact such a tool may have on performance in two types of distributed execution-flows, one for linear algebra with a blocked matrix multiplication application and the other in the context of data-clustering with a k-means application. We show that with very little modification of the original decorator (3 lines of code to be modified) of the task-based application the gain in performance can rise above 40% for tasks relying heavily on data reuse on a distributed environment, especially when loading the data is prominent in the execution time.

关键词： Task analysis Python Memory management Runtime Libraries Tools Metadata

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Modern applications of machine learning in quantum sciences

arXiv

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arXiv 2022年

作者： Dawid, Anna Arnold, Julian Requena, Borja Gresch, Alexander Plodzien, Marcin Donatella, Kaelan Nicoli, Kim A. Stornati, Paolo Koch, Rouven Büttner, Miriam Okula, Robert Muñoz–Gil, Gorka Vargas–Hernández, Rodrigo A. Cervera-Lierta, Alba Carrasquilla, Juan Dunjko, Vedran Gabrié, Marylou Huembeli, Patrick van Nieuwenburg, Evert Vicentini, Filippo Wang, Lei Wetzel, Sebastian J. Carleo, Giuseppe Greplová, Eliška Krems, Roman Marquardt, Florian Tomza, Michal Lewenstein, Maciej Dauphin, Alexandre Faculty of Physics University of Warsaw Poland ICFO - Institut de Ciències Fotòniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain Center for Computational Quantum Physics Flatiron Institute New York United States Department of Physics University of Basel Switzerland Institute for Theoretical Physics Heinrich Heine University Düsseldorf Germany Institute for Quantum Inspired and Quantum Optimization Hamburg University of Technology Germany Université de Paris CNRS Laboratoire Matériaux et Phénomènes Quantiques France Machine Learning Group Technische Universität Berlin Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Department of Applied Physics Aalto University Espoo Finland Institute of Physics Albert-Ludwig University of Freiburg Germany International Centre for Theory of Quantum Technologies University of Gdańsk Poland Department of Algorithms and System Modeling Faculty of Electronics Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Poland Institute for Theoretical Physics University of Innsbruck Austria Department of Chemistry University of Toronto Canada Vector Institute for Artificial Intelligence MaRS Centre Toronto Canada Department of Chemistry and Chemical Biology McMaster University Hamilton Canada Barcelona Supercomputing Center Spain LIACS Leiden University Netherlands CMAP École Polytechnique France Switzerland Menten AI Inc. Palo AltoCA United States Niels Bohr Institute Copenhagen Denmark CPHT CNRS École Polytechnique Institut Polytechnique de Paris PalaiseauF-91128 France Beijing National Lab for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Songshan Lake Materials Laboratory Dongguan China Perimeter Institute for Theoretical Physics Waterloo Canada Kavli Institute of Nanoscience Delft University of Technology DelftNL-2600 GA Netherlands Department of

In this book, we provide a comprehensive introduction to the most recentadvances in the application of machine learning methods in quantum sciences. Wecover the use of deep learning and kernel methods in supervised, unsupervised,and reinforcement learning algorithms for phase classification, representationof many-body quantum states, quantum feedback control, and quantum circuitsoptimization. Moreover, we introduce and discuss more specialized topics suchas differentiable programming, generative models, statistical approach tomachine learning, and quantum machine learning. © 2022, CC BY.

关键词： Deep learning

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Solving the Klein-Gordon equation using Fourier spectral methods: A benchmark test for computer performance 23

Solving the Klein-Gordon equation using Fourier spectral met...

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23rd High Performance Computing Symposium, HPC 2015, Part of the 2015 Spring Simulation Multi-Conference, SpringSim 2015

作者： Aseeri, S. Batrašev, O. Icardi, M. Leu, B. Liu, A. Li, N. Muite, B.K. Müller, E. Palen, B. Quell, M. Servat, H. Sheth, P. Speck, R. Van Moer, M. Vienne, J. KAUST Supcrcomputing Laboratory 4700 King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Arvutitcaduse Instituut Tartu Ülikool Liivi 2 Tartu50409 Estonia Stochastic Numerics Group 4700 King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Electrical and Computer Engineering University of Michigan Ann ArborMI48109-2122 United States Numerical Algorithms Group Wilkinson House Jordan Hill Road Oxford0X2 8DR United Kingdom Numerical Mathematics Group 4700 King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematical Sciences University of Bath BathBA2 7 AY United Kingdom CAEN Advanced Computing University of Michigan Ann ArborMI48109-2094 United States Lnstitut für Analysis und Scientific Computing Technische Universität Wien Wiedner Haupstrasse 8-10 Wien1040 Austria Compulcr Sciences Department BSC UPC c/Jordi Girona Barcelona Calalunya08034 Spain Biomedical Engineering University of Michigan Ann ArborMI48109-2122 United States Jülich Supercomputing Centre Forschungszentrum Jülich GmbH Germany Germany National Center for Supercomputing Applications University of Illinois at Urbiina-Chanipaign UrbanaIL61801 United States Texas Advanced Computing Center University of Texas at Austin AustinTX78758-4497 United States

ISBN: (纸本)9781510810600

The cubic Klein-Gordon equation is a simple but non-trivial partial differential equation whose numerical solution has the main building blocks required for the solution of many other partial differential equations. In this study, the library 2DE-COMP&FFT is used in a Fourier spectral scheme to solve the Klein-Gordon equation and strong scaling of the code is examined on thirteen different machines for a problem size of 5123. The results are useful in assessing likely performance of other parallel fast Fourier transform based programs for solving partial differential equations. The problem is chosen to be large enough to solve on a workstation, yet also of interest to solve quickly on a supercomputer, in particular for parametric studies. Unlike the Linpack benchmark, a high ranking will not be obtained by simply building a bigger computer. © 2015 Aseeri et al.

关键词： Fast Fourier transforms

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Solving the Klein-Gordon equation using fourier spectral methods: a benchmark test for computer performance 15

Solving the Klein-Gordon equation using fourier spectral met...

引用

Proceedings of the Symposium on High Performance Computing

作者： S. Aseeri O. Batrasev M. Icardi B. Leu A. Liu N. Li B. K. Muite E. Müller B. Palen M. Quell H. Servat P. Sheth R. Speck M. Van Moer J. Vienne King Abdullah University of Science and Technology Thuwal Kingdom of Saudi Arabia Arvutiteaduse instituut Tartu Estonia and Tartu Ülikool University of Michigan Ann Arbor MI Numerical Algorithms Group Oxford United Kingdom Arvutiteaduse instituut Tartu Estonia and Tartu Ülikool and King Abdullah University of Science and Technology University of Bath Bath United Kingdom Technische Universität Wien Wien Austria Barcelona Supercomputer Center Polytechnic University of Catalonia (UPC) Jülich Supercomputing Centre Germany University of Illinois at Urbana-Champaign Urbana IL University of Texas at Austin Austin Texas

ISBN: (纸本)9781510801011

The cubic Klein-Gordon equation is a simple but non-trivial partial differential equation whose numerical solution has the main building blocks required for the solution of many other partial differential equations. In this study, the library 2DECOMP&FFT is used in a Fourier spectral scheme to solve the Klein-Gordon equation and strong scaling of the code is examined on thirteen different machines for a problem size of 5123. The results are useful in assessing likely performance of other parallel fast Fourier transform based programs for solving partial differential equations. The problem is chosen to be large enough to solve on a workstation, yet also of interest to solve quickly on a supercomputer, in particular for parametric studies. Unlike the Linpack benchmark, a high ranking will not be obtained by simply building a bigger computer.

关键词： benchmarks

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A generic model for ridges: A new framework to characterise biological planar structures

A generic model for ridges: A new framework to characterise ...

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IEEE International Symposium on Biomedical Imaging

作者： Antonio Martinez-Sanchez Inmaculada Garcia Jose-Jesus Fernandez Supercomputing and Algorithms Group University of Almeria Almeria Spain Supercomputing and Algorithms Group University of Malaga Malaga Spain National Centre for Biotechnology (CNB-CSIC) Madrid Spain

ISBN: (纸本)9781457718571

This work introduces a new generic framework to the analysis of ridges and its application to segmentation of planar structures in bioimaging. Based on a Gaussian model, local information associated to ridges is extracted, which actually characterise them. The theoretical basis of the framework is first presented for the one-dimensional case. Its extension to higher dimensions is then shown to be straightforward by applying the same basis over the maximum curvature direction. In the particular case of 3D, this framework allows characterisation of planar structures. The potential of the methodology to segment planar structures in 3D bioimaging is illustrated with several examples.

关键词： Image segmentation Biomembranes Manifolds Mice Heart Biological system modeling

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Dynamic Load Scheduling on CPU-GPU for Iterative Tomographic Reconstruction

Dynamic Load Scheduling on CPU-GPU for Iterative Tomographic...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： J.I. Agulleiro F. V'zquez E.M. Garzón J.J. Fern'ndez Supercomputing and Algorithms Group Associated Unit CSIC-UAL Agrifood Campus of International Excellence (ceiA3) University of Almería Almeria Spain Supercomputing and Algorithms Group Agrifood Campus of International Excellence (ceiA3) Almería Spain

This work presents a hybrid computing approach which combines GPUs and multicore processors to fully take advantage of the computing power latent in modern computers. It also presents its application to the problem of tomographic reconstruction. One inherent characteristic of these modern platforms is their heterogeneity, which raises the issue of workload distribution among the different processing elements. Adaptive load balancing techniques are thus necessary to properly adjust the amount of work to be done by each computing element. Here, we have chosen the 'on-demand' strategy, a well-known technique in the HPC field by which the different elements asynchronously request a piece of work when they become idle, thereby keeping the system fairly well balanced. The results show that our scheme accommodates to the heterogeneous platform where it runs as it assigns more work to the faster processing elements automatically, which allows to correctly exploit all the resources available and to get complete reconstructions in less time than pure CPU or GPU approaches.

关键词： Graphics processing unit Multicore processing Central Processing Unit Slabs Computers Instruction sets

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Fast Sparse Matrix Matrix Product Based on ELLR-T and GPU Computing

Fast Sparse Matrix Matrix Product Based on ELLR-T and GPU Co...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： F. V'zquez G. Ortega J.J. Fern'ndez I. García E.M. Garzón Supercomputing and Algorithms Group Associated Unit CSIC-UAL University of Almería Almeria Spain Supercomputing and Algorithms Group Associated Unit CSIC-UAL Agrifood Campus of International Excellence (ceiA3) Ctra Almería Spain Dpt. of Comput. Archit Univ. of Málaga Málaga Spain

A wide range of applications in engineering and scientific computing are based on the computation of matrices products, where one of them is sparse. The computational requirements of these operations are very high when dimensions of the matrices increase. The goal of this work is the acceleration of the sparse matrix matrix product (SpMM) on Graphics Processing Units (GPUs). The operation SpMM can be computed by a set of sparse matrix vector operations (SpMV). However, this approach does not reach optimal performance because it cannot benefit from the large value of the ratio computation/memory access associated to the SpMM operation. In this work a routine called FastSpMM is described and its performance evaluated. FastSpMM can be considered as an extension of the ELLRT routine to compute SpMV on GPUs which is based on the ELLPACK-R storage format for sparse matrices. FastSpMM combines the high ratio computation/memory access with the advantages of ELLR-T to exploit the GPU architecture. The CUSPARSE library, supplied by NVIDIA, which also includes routines to compute SpMM on GPUs is used in this work as a reference for performance comparison. Experimental evaluations based on a representative set of test matrices show that FastSpMM outperforms the corresponding CUSPARSE routine in terms of performance.

关键词： Sparse matrices Graphics processing unit Instruction sets Computer architecture Libraries Acceleration Vectors

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Meandre: semantic-driven data-intensive flows in the clouds

Meandre: semantic-driven data-intensive flows in the clouds

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4th IEEE International Conference on eScience, eScience 2008

作者： Llorà, Xavier Ács, Bernie Auvil, Loretta S. Capitanu, Boris Welge, Michael E. Goldberg, David E. Data-Intensive Technologies and Applications National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Urbana IL 61801 United States Automated Learning Group National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Urbana IL 61801 United States Illinois Genetic Algorithms Laboratory Dept. of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 United States

ISBN: (纸本)9780769535357

Data-intensive flow computing allows efficient processing of large volumes of data otherwise unapproachable. This paper introduces a new semantic-driven data-intensive flow infrastructure which: (1) provides a robust and transparent scalable solution from a laptop to large-scale clusters,(2) creates an unified solution for batch and interactive tasks in high-performance computing environments, and (3) encourages reusing and sharing components. Banking on virtualization and cloud computing techniques the Meandre infrastructure is able to create and dispose Meandre clusters on demand, being transparent to the final user. This paper also presents a prototype of such clustered infrastructure and some results obtained using it. © 2008 IEEE.

关键词： Semantics

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