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检索条件"任意字段=9th IEEE/ACM Workshop on Python for High-Performance and Scientific Computing, PYHPC 2020"

共 18 条记录，以下是1-10 订阅

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Proceedings of pyhpc 2020: 9th workshop on python for high-performance and scientific computing, Held in conjunction with SC 2020: the International Conference for high performance computing, Networking, Storage and Analysis

Proceedings of PYHPC 2020: 9th Workshop on Python for High-P...

引用

9th ieee/acm workshop on python for high-performance and scientific computing, pyhpc 2020

ISBN: (纸本)9780738110868

the proceedings contain 6 papers. the topics discussed include: accelerating microstructural analytics with Dask for volumetric x-ray images;enabling system wide shared memory for performance improvement in PyCOMPSs applications;experiences in developing a distributed agent-based modeling toolkit with python;data engineering for HPC with python;python workflows on HPC systems;and distributed asynchronous array computing with the jetlag environment.

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python Workflows on HPC Systems 9

Python Workflows on HPC Systems

引用

9th workshop on python for high-performance and scientific computing (pyhpc)

作者： Strassel, Dominik Reusch, Philipp Keuper, Janis Fraunhofer ITWM CC HPC Kaiserslautern Germany Offenburg Univ Inst Machine Learning & Analyt Offenburg Germany

ISBN: (纸本)9780738110868

the recent successes and wide spread application of compute intensive machine learning and data analytics methods have been boosting the usage of the python programming language on HPC systems. While python provides many advantages for the users, it has not been designed with a focus on multi-user environments or parallel programming - making it quite challenging to maintain stable and secure python workflows on a HPC system. In this paper, we analyze the key problems induced by the usage of python on HPC clusters and sketch appropriate workarounds for efficiently maintaining multi-user python software environments, securing and restricting resources of python jobs and containing python processes, while focusing on Deep Learning applications running on GPU clusters.

关键词： high performance computing python deep learning machine learning data analytics

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Experiences in Developing a Distributed Agent-based Modeling Toolkit with python 9

Experiences in Developing a Distributed Agent-based Modeling...

引用

9th workshop on python for high-performance and scientific computing (pyhpc)

作者： Collier, Nicholson T. Ozik, Jonathan Tatara, Eric R. Argonne Natl Lab Decis & Infrastruct Sci Lemont IL 60439 USA

ISBN: (纸本)9780738110868

Distributed agent-based modeling (ABM) on high-performance computing resources provides the promise of capturing unprecedented details of large-scale complex systems. However, the specialized knowledge required for developing such ABMs creates barriers to wider adoption and utilization. Here we present our experiences in developing an initial implementation of Repast4Py, a python-based distributed ABM toolkit. We build on our experiences in developing ABM toolkits, including Repast for high performance computing (Repast HPC), to identify the key elements of a useful distributed ABM toolkit. We leverage the Numba, NumPy, and PyTorch packages and the python C-API to create a scalable modeling system that can exploit the largest HPC resources and emerging computing architectures.

关键词： agent-based modeling parallel processing high performance computing computer simulation

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Distributed Asynchronous Array computing with the JetLag Environment 9

Distributed Asynchronous Array Computing with the JetLag Env...

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9th workshop on python for high-performance and scientific computing (pyhpc)

作者： Brandt, Steven R. Hasheminezhad, Bita Wu, Nanmiao Sakin, Sayef Azad Bigelow, Alex R. Isaacs, Katherine E. Huck, Kevin Kaiser, Hartmut Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Univ Arizona Dept Comp Sci Tucson AZ 85721 USA Univ Oregon OACISS Eugene OR 97403 USA

ISBN: (纸本)9780738110868

We describe JetLag, a python-based environment that provides access to a distributed, interactive, asynchronous many-task (AMT) computing framework called Phylanx. this environment encompasses the entire computing process, from a Jupyter front-end for managing code and results to the collection and visualization of performance data. We use a python decorator to access the abstract syntax tree of python functions and transpile them into a set of C++ data structures which are then executed by the HPX runtime. the environment includes services for sending functions and their arguments to run as jobs on remote resources. A set of Docker and Singularity containers are used to simplify the setup of the JetLag environment. the JetLag system is suitable for a variety of array computational tasks, including machine learning and exploratory data analysis.

关键词： array computing asynchronous distributed python

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Data Engineering for HPC with python 9

Data Engineering for HPC with Python

引用

9th workshop on python for high-performance and scientific computing (pyhpc)

作者： Abeykoon, Vibhatha Perera, Niranda Widanage, Chathura Kamburugamuve, Supun Kanewalat, thejaka Amila Maithree, Hasara Wickramasinghe, Pulasthi Uyar, Ahmet Fox, Geoffrey Luddy Sch Informat Comp & Engn Bloomington IN 47408 USA Digital Sci Ctr Bloomington IN 47408 USA Indiana Univ Alumni Bloomington IN 47408 USA Univ Moratuwa Dept Comp Sci & Engn Moratuwa Sri Lanka

ISBN: (纸本)9780738110868

Data engineering is becoming an increasingly important part of scientific discoveries with the adoption of deep learning and machine learning. Data engineering deals with a variety of data formats, storage, data extraction, transformation, and data movements. One goal of data engineering is to transform data from original data to vector/matrix/tensor formats accepted by deep learning and machine learning applications. there are many structures such as tables, graphs, and trees to represent data in these data engineering phases. Among them, tables are a versatile and commonly used format to load and process data. In this paper, we present a distributed python API based on table abstraction for representing and processing data. Unlike existing state-of-the-art data engineering tools written purely in python, our solution adopts high performance compute kernels in C++, with an in-memory table representation with Cython-based python bindings. In the core system, we use MPI for distributed memory computations with a data-parallel approach for processing large datasets in HPC clusters.

关键词： python MPI HPC Data Engineering

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Accelerating Microstructural Analytics with Dask for Volumetric X-ray Images 9

Accelerating Microstructural Analytics with Dask for Volumet...

引用

9th workshop on python for high-performance and scientific computing (pyhpc)

作者： Ushizima, Daniela McCormick, Matthew Parkinson, Dilworth Univ Calif Berkeley Computat Res Div LBNL Berkeley Inst Data Sci Berkeley CA 94720 USA Kitware Inc Insight Toolkit Carrboro NC 27510 USA LBNL Adv Light Source Beamline 8 3 2 Berkeley CA 94720 USA

ISBN: (纸本)9780738110868

While X-ray microtomography has become indispensable in 3D inspections of materials, efficient processing of such volumetric datasets continues to be a challenge. this paper describes a computational environment for HPC to facilitate parallelization of algorithms in computer vision and machine learning needed for microstructure characterization and interpretation. the contribution is to accelerate microstructural analytics by employing Dask high-level parallel abstractions, which scales Numpy workflows to enable multi-dimensional image analysis of diverse specimens. We illustrate our results using an example from materials sciences, emphasizing the benefits of parallel execution of image-dependent tasks. Preliminary results show that the proposed environment configuration and scientific software stack deployed using JupyterLab at NERSC Cori enables near-real time analyses of complex, high-resolution experiments.

关键词： MicroCT analysis python Dask HPC

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Enabling System Wide Shared Memory for performance Improvement in PyCOMPSs Applications 9

Enabling System Wide Shared Memory for Performance Improveme...

引用

9th workshop on python for high-performance and scientific computing (pyhpc)

作者： Foyer, Clement Conejero, Javier Ejarque, Jorge Badia, Rosa M. Tate, Adrian McIntosh-Smith, Simon HPE HPC AI EMEA Res Lab Bristol Avon England Barcelona Supercomp Ctr Barcelona Spain Numer Algorithms Grp Ltd NAG Oxford England Univ Bristol Dept Comp Sci High Performance Comp Res Grp Bristol Avon England

ISBN: (纸本)9780738110868

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： Memory Shared Memory Task python Parallel Programming Distributed Memory NumPy Data Management

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Dispel4py: A python framework for data-intensive eScience 5

Dispel4py: A python framework for data-intensive eScience

引用

5th workshop on python for high-performance and scientific computing, pyhpc 2015

作者： Krause, Amrey Filgueira, Rosa Atkinson, Malcolm EPCC University of Edinburgh EdinburghEH9 3FD United Kingdom School of Informatics University of Edinburgh EdinburghEH8 9LE United Kingdom

ISBN: (纸本)9781450340106

We present dispel4py, a novel data intensive and high performance computing middleware provided as a standard python library for describing stream-based workows. It allows its users to develop their scientific applications locally and then run them on a wide range of HPC-infrastructures without any changes to the code. Moreover, it provides automated and efficient parallel mappings toMPI, multiprocessing, Storm and Spark frameworks, commonly used in big data applications. It builds on the wide availability of python in many environments and only requires familiarity with basic python syntax. We will show the dispel4py advantages by walking through an example. We will conclude demonstrating how dispel4py can be employed as an easy-to-use tool for designing scientific applications using real-world scenarios. © 2015 acm.

关键词： python

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Enabling python to execute efficiently in heterogeneous distributed infrastructures with PyCOMPSs 7

Enabling Python to execute efficiently in heterogeneous dist...

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7th workshop on python for high-performance and scientific computing (pyhpc)

作者： Amela, Ramon Ramon-Cortes, Cristian Ejarque, Jorge Conejero, Javier Badia, Rosa M. Barcelona Supercomp Ctr Barcelona Spain CSIC Barcelona Spain

ISBN: (纸本)9781450351249

python has been adopted as programming language by a large number of scientific communities. Additionally to the easy programming interface, the large number of libraries and modules that have been made available by a large number of contributors, have taken this language to the top of the list of the most popular programming languages in scientific applications. However, one main drawback of python is the lack of support for concurrency or parallelism. PyCOMPSs is a proved approach to support task-based parallelism in python that enables applications to be executed in parallel in distributed computing platforms. this paper presents PyCOMPSs and how it has been tailored to execute tasks in heterogeneous and multi-threaded environments. We present an approach to combine the task-level parallelism provided by PyCOMPSs with the thread-level parallelism provided by MKL. performance and behavioral results in distributed computing heterogeneous clusters show the benefits and capabilities of PyCOMPSs in both HPC and Big Data infrastructures.

关键词： HPC python Big Data Linear Algebra Heterogeneous infrastructures

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workshop Organization

Workshop Organization

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workshop on python for high-performance and scientific computing (pyhpc)

Provides a listing of current committee members and society officers.

ISBN: (数字)9780738110868

ISBN: (纸本)9781665422864

Provides a listing of current committee members and society officers.

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