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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11-20 订阅

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python Workflows on HPC Systems

Python Workflows on HPC Systems

引用

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

作者： Dominik Straßel Philipp Reusch Janis Keuper Fraunhofer ITWM Kaiserslautern Germany Offenburg University Germany

ISBN: (数字)9780738110868

ISBN: (纸本)9781665422864

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： python Graphics processing units Machine learning Kernel Linux Standards Software

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

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

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

作者： Nicholson T. Collier Jonathan Ozik Eric R. Tatara Argonne National Laboratory Lemont IL USA

ISBN: (数字)9780738110868

ISBN: (纸本)9781665422864

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： C++ languages Statistics Sociology Context modeling Strips Java Graphics processing units

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Distributed Asynchronous Array computing with the JetLag Environment

Distributed Asynchronous Array Computing with the JetLag Env...

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

作者： Steven R. Brandt Bita Hasheminezhad Nanmiao Wu Sayef Azad Sakin Alex R. Bigelow Katherine E. Isaacs Kevin Huck Hartmut Kaiser Louisiana State University Baton Rouge LA USA University of Arizona Tucson AZ USA University of Oregon Eugene OR USA

ISBN: (数字)9780738110868

ISBN: (纸本)9781665422864

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 *** 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： Arrays C++ languages Distributed databases python Runtime Parallel processing Libraries

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Accelerating Microstructural Analytics with Dask for Volumetric X-ray Images

Accelerating Microstructural Analytics with Dask for Volumet...

引用

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

作者： Daniela Ushizima Matthew McCormick Dilworth Parkinson Computational Research Division LBNL UC Berkeley Berkeley CA USA 94720 Insight Toolkit Carrboro NC USA 27510 Beamline 8.3.2 Berkeley CA USA 94720

ISBN: (数字)9780738110868

ISBN: (纸本)9781665422864

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： three-dimensional displays python Arrays Software Image segmentation X-ray imaging Image analysis

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Data Engineering for HPC with python

Data Engineering for HPC with Python

引用

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

作者： Vibhatha Abeykoon Niranda Perera Chathura Widanage Supun Kamburugamuve thejaka Amila Kanewala Hasara Maithree Pulasthi Wickramasinghe Ahmet Uyar Geoffrey Fox Luddy School of Informatics Computing and Engineering IN 47408 USA Digital Science Center Bloomington IN 47408 USA Indiana University Alumni IN 47408 USA University of Moratuwa Department of Computer Science and Engineering Sri Lanka

ISBN: (数字)9780738110868

ISBN: (纸本)9781665422864

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： Data engineering python Kernel Big Data Data analysis Data models Runtime

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Enabling System Wide Shared Memory for performance Improvement in PyCOMPSs Applications

Enabling System Wide Shared Memory for Performance Improveme...

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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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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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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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