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Proceedings of pyhpc 2016: 6th workshop on python for high-performance and scientific computing - Held in conjunction with SC16: the International Conference for high performance computing, Networking, Storage and Analysis

Proceedings of PyHPC 2016: 6th Workshop on Python for High-P...

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6th workshop on python for high-performance and scientific computing, pyhpc 2016

ISBN: (纸本)9781509052202

the proceedings contain 10 papers. the topics discussed include: migrating legacy Fortran to python while retaining Fortran-level performance through transpilation and type hints;PALLADIO: a parallel framework for robust variable selection in high-dimensional data;high-performance python-C++ bindings with PyPy and Cling;a new architecture for optimization modeling frameworks;performance of MPI codes written in python with NumPy and mpi4py;boosting python performance on Intel processors: a case study of optimizing music recognition;epython: an implementation of python for the many-core epiphany co-processor;Devito: towards a generic finite difference DSL using symbolic python;and Mrs: high performance MapReduce for iterative and asynchronous algorithms in python.

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pyhpc2016: 6th workshop on python for high-performance and scientific computing

Proceedings of PyHPC 2016: 6th Workshop on Python for High-P...

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Proceedings of pyhpc 2016: 6th workshop on python for high-performance and scientific computing - Held in conjunction with SC16: the International Conference for high performance computing, Networking, Storage and Analysis 2016年 v-vi页

作者： Schreiber, Andreas Scullin, William Spotz, Bill Terrel, Andy R. Basermann, Achim Chen, Yung-Yu Foley, S. Harrison, Cyrus Hinsen, Konrad Klemm, Michael Kloeckner, Andreas Ling, Maurice Muller, Mike Germany Argonne National Laboratory United States Sandia National Laboratories United States Fashion Metric Inc. United States SOLVCON Project United States University of Wisconsin-La Crosse United States Lawrence Livermore National Laboratory United States CNRS France Intel Corporation China University of Illinois at Urbana-Champaign United States University of Melbourne Australia Python Academy Australia

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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...

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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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Proceedings of pyhpc 2015: 5th workshop on python for high-performance and scientific computing - Held in conjunction with SC 2015: the International Conference for high performance computing, Networking, Storage and Analysis

Proceedings of PyHPC 2015: 5th Workshop on Python for High-P...

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

ISBN: (纸本)9781450340106

the proceedings contain 6 papers. the topics discussed include: python, performance and natural language processing;performance and productivity of parallel python programming - a study with a CFD test case;PeachPy meets Opcodes: direct machine code generation from python;PyTACC: HPC python at the Texas advanced computing center;feature frequency profiles for automatic sample identification using PySpark;dispel4py: a python framework for data-intensive eScience;and solving large numerical optimization problems in HPC with python.

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performance of MPI codes written in python with NumPy and mpi4py 6

Performance of MPI codes written in python with NumPy and mp...

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6th workshop on python for high-performance and scientific computing, pyhpc 2016

作者： Smith, Ross Engility Corporation Aberdeen Proving Ground United States

ISBN: (纸本)9781509052202

python is an interpreted language that has become more commonly used within HPC applications. python benefits from the ability to write extension modules in C, which can further use optimized libraries that have been written in other compiled languages. For HPC users, two of the most common extensions are NumPy and mpi4py. It is possible to write a full computational kernel in a compiled language and then build that kernel into an extension module. However, this process requires not only the kernel be written in the compiled language, but also the interface between the kernel and python be implemented. If possible, it would be preferable to achieve similar performance by writing the code directly in python using readily available performant modules. In this work the performance differences between compiled codes and codes written using python3 and commonly available modules, most notably NumPy and mpi4py, are investigated. Additionally, the performance of an open source python stack is compared to the recently announced Intel python3 distribution. © 2016 IEEE.

关键词： python

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Migrating legacy fortran to python while retaining fortran-level performance through transpilation and type hints 6

Migrating legacy fortran to python while retaining fortran-l...

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6th workshop on python for high-performance and scientific computing, pyhpc 2016

作者： Bysiek, Mateusz Drozd, Aleksandr Matsuoka, Satoshi Tokyo Institute of Technology Meguro Tokyo1528550 Japan

ISBN: (纸本)9781509052202

We propose a method of accelerating python code by just-in-time compilation leveraging type hints mechanism introduced in python 3.5. In our approach performance-critical kernels are expected to be written as if python was a strictly typed language, however without the need to extend python syntax. this approach can be applied to any python application, however we focus on a special case when legacy Fortran applications are automatically translated into python for easier maintenance. We developed a framework implementing two-way transpilation and achieved performance equivalent to that of python manually translated to Fortran, and better than using other currently available JIT alternatives (up to 5x times faster than Numba in some experiments). © 2016 IEEE.

关键词： python

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Boosting python performance on Intel processors: A case study of optimizing music recognition 6

Boosting python performance on Intel processors: A case stud...

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6th workshop on python for high-performance and scientific computing, pyhpc 2016

作者： Li, Yuanzhe Schwiebert, Loren Wayne State University DetroitMI48202 United States

ISBN: (纸本)9781509052202

We present a case study of optimizing a python-based music recognition application on Intel Haswell Xeon processor. With support from Numpy and Scipy, python addresses the requirements of the music recognition problem with math library utilization and special structures for data access. However, a general optimized python application cannot fully utilize the latest high performance multicore processors. In this study, we survey an existing open-source music recognition application, written in python, to explore the effect of applying changes to the Scipy and Numpy libraries to achieve full processor resource occupancy and reduce code latency. Instead of comparing across many different architectures, we focus on Intel high performance processors that have multiple cores and vector registers, and we attempt to preserve both user-friendliness and code scalability so that the revised library functions can be ported to other platforms and require no additional code changes. © 2016 IEEE.

关键词： python

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Epython: An implementation of python for the many-core epiphany co-processor 6

EPython: An implementation of python for the many-core epiph...

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6th workshop on python for high-performance and scientific computing, pyhpc 2016

作者： Brown, Nick James Clerk Maxwell Building Edinburgh United Kingdom

ISBN: (纸本)9781509052202

the Epiphany is a many-core, low power, low on-chip memory architecture and one can very cheaply gain access to a number of parallel cores which is beneficial for HPC education and prototyping. the very low power nature of these architectures also means that there is potential for their use in future HPC machines, however there is a high barrier to entry in programming them due to the associated complexities and immaturity of supporting *** this paper we present our work on epython, a subset of python for the Epiphany and similar many-core co-processors. Due to the limited on-chip memory per core we have developed a new python interpreter and this, combined with additional support for parallelism, has meant that novices can take advantage of python to very quickly write parallel codes on the Epiphany and explore concepts of HPC using a smaller scale parallel machine. the high level nature of python opens up new possibilities on the Epiphany, we examine a computationally intensive Gauss-Seidel code from the programmability and performance perspective, discuss running python hybrid on both the host CPU and Epiphany, and interoperability between a full python interpreter on the CPU and epython on the Epiphany. the result of this work is support for developing python on the Epiphany, which can be applied to other similar architectures, that the community have already started to adopt and use to explore concepts of parallelism and HPC. © 2016 IEEE.

关键词： python

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Devito: Towards a generic finite difference DSL using symbolic python 6

Devito: Towards a generic finite difference DSL using symbol...

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6th workshop on python for high-performance and scientific computing, pyhpc 2016

作者： Lange, Michael Kukreja, Navjot Louboutin, Mathias Luporini, Fabio Vieira, Felippe Pandolfo, Vincenzo Velesko, Paulius Kazakas, Paulius Gorman, Gerard Department of Earth Science and Engineering Imperial College United Kingdom SENAI CIMATEC Salvador Brazil Seismic Lab. for Imaging and Modeling University of British Columbia Canada Department of Computer Science Imperial College London United Kingdom College of Electrical and Computer Engineering University of Oklahoma United States Department of Computer Science University of York United Kingdom

ISBN: (纸本)9781509052202

Domain specific languages (DSL) have been used in a variety of fields to express complex scientific problems in a concise manner and provide automated performance optimization for a range of computational architectures. As such DSLs provide a powerful mechanism to speed up scientific python computation that goes beyond traditional vectorization and pre-compilation approaches, while allowing domain scientists to build applications within the comforts of the python software ecosystem. In this paper we present Devito, a new finite difference DSL that provides optimized stencil computation from high-level problem specifications based on symbolic python expressions. We demonstrate Devito's symbolic API and performance advantages over traditional python acceleration methods before highlighting its use in the scientific context of seismic inversion problems. © 2016 IEEE.

关键词： python

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Mrs: high performance MapReduce for iterative and asynchronous algorithms in python 6

Mrs: High performance MapReduce for iterative and asynchrono...

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6th workshop on python for high-performance and scientific computing, pyhpc 2016

作者： Lund, Jeffrey Ashcraft, Chace McNabb, Andrew Seppi, Kevin Computer Science Department Brigham Young University ProvoUT84604 United States

ISBN: (纸本)9781509052202

Mrs [1] is a lightweight python-based MapReduce implementation designed to make MapReduce programs easy to write and quick to run, particularly useful for research and academia. A common set of algorithms that would benefit from Mrs are iterative algorithms, like those frequently found in machine learning;however, iterative algorithms typically perform poorly in the MapReduce framework, meaning potentially poor performance in Mrs as ***, we propose four modifications to the original Mrs with the intent to improve its ability to perform iterative algorithms. First, we used direct task-to-task communication for most iterations and only occasionally write to a distributed file system to preserve fault tolerance. Second, we combine the reduce and map tasks which span successive iterations to eliminate unnecessary communication and scheduling latency. third, we propose a generator-callback programming model to allow for greater flexibility in the scheduling of tasks. Finally, some iterative algorithms are naturally expressed in terms of asynchronous message passing, so we propose a fully asynchronous variant of *** then demonstrate Mrs' enhanced performance in the context of two iterative applications: particle swarm optimization (PSO), and expectation maximization (EM). © 2016 IEEE.

关键词： MapReduce

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