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检索条件"任意字段=6th Workshop on Python for High-Performance and Scientific Computing, PyHPC 2016"

共 24 条记录，以下是11-20 订阅

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high-performance python-C++ bindings with PyPy and cling 6

High-performance python-C++ bindings with PyPy and cling

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

作者： Lavrijsen, Wim T.L.P. Dutta, Aditi Lawrence Berkeley National Laboratory BerkeleyCA United States Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781509052202

the use of python as a high level productivity language on top of high performance libraries written in C++ requires efficient, highly functional, and easy-to-use cross-language bindings. C++ was standardized in 1998 and up until 2011 it saw only one minor revision. Since then, the pace of revisions has increased considerably, with a lot of improvements made to expressing semantic intent in interface definitions. For automatic python-C++ bindings generators it is both the worst of times, as parsers need to keep up, and the best of times, as important information such as object ownership and thread safety can now be expressed. We present cppyy, which uses Cling, the Clang/LLVM-based C++ interpreter, to automatically generate python-C++ bindings for PyPy. Cling provides dynamic access to a modern C++ parser and PyPy brings a full toolbox of dynamic optimizations for high performance. the use of Cling for parsing, provides up-to-date C++ support now and in the foreseeable future. We show that with PyPy the overhead of calls to C++ functions from python can be reduced by an order of magnitude compared to the equivalent in Cpython, making it sufficiently low to be unmeasurable for all but the shortest C++ functions. Similarly, access to data in C++ is reduced by two orders of magnitude over access from Cpython. Our approach requires no intermediate language and more pythonistic presentations of the C++ libraries can be written in python itself, with little performance cost due to inlining by PyPy. this allows for future dynamic optimizations to be fully transparent. © 2016 IEEE.

关键词： python

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Dynamic provisioning and execution of HPC workflows using python

Dynamic provisioning and execution of HPC workflows using py...

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

作者： Harris, Chris O'Leary, Patrick Grauer, Michael Chaudhary, Aashish Kotfila, Chris O'Bara, Robert Kitware Inc Clifton ParkNY12065 United States

ISBN: (纸本)9781509052202

high-performance computing (HPC) workflows over the last several decades have proven to assist in the understanding of scientific phenomena and the production of better products, more quickly, and at reduced cost. However, HPC workflows are difficult to implement and use for a variety of reasons. In this paper, we describe the development of the python-based cumulus, which addresses many of these barriers. cumulus is a platform for the dynamic provisioning and execution of HPC workflows. cumulus provides the infrastructure needed to build applications that leverage traditional or Cloud-based HPC resources in their workflows. Finally, we demonstrate the use of cumulus in both web and desktop simulation applications, as well as in an Apache Spark-based analysis application. © 2016 IEEE.

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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Migrating Legacy Fortran to python While Retaining Fortran-Level performance through Transpilation and Type Hints

Migrating Legacy Fortran to Python While Retaining Fortran-L...

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

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

ISBN: (纸本)9781509052219

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).

关键词： Kernel Programming Optimization Interoperability C++ languages Context Standards

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Boosting python performance on Intel Processors: A Case Study of Optimizing Music Recognition

Boosting Python Performance on Intel Processors: A Case Stud...

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

作者： Yuanzhe Li Loren Schwiebert Wayne State University Detroit Michigan

ISBN: (纸本)9781509052219

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.

关键词： Multithreading Libraries Instruction sets Graphics processing units Registers

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Dynamic Provisioning and Execution of HPC Workflows Using python

Dynamic Provisioning and Execution of HPC Workflows Using Py...

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

作者： Chris Harris Patrick O'Leary Michael Grauer Aashish Chaudhary Chris Kotfila Robert O'Bara Kitware Inc Clifton Park NY

ISBN: (纸本)9781509052219

关键词： Structural beams Electronic mail Cloud computing Dynamic scheduling Complexity theory Open source software

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performance Evaluation of python Parallel Programming Models: And mpi4py 6

Performance Evaluation of Python Parallel Programming Models...

引用

6th International IEEE/ACM workshop on Extreme Scale Programming Models and Middleware, ESPM2 2021

作者： Fink, Zane Liu, Simeng Choi, Jaemin Diener, Matthias Kale, Laxmikant V. University of Illinois at Urbana-Champaign Department of Computer Science UrbanaIL United States

ISBN: (纸本)9781665411400

python is rapidly becoming the lingua franca of machine learning and scientific computing. With the broad use of frameworks such as Numpy, SciPy, and TensorFlow, scientific computing and machine learning are seeing a productivity boost on systems without a requisite loss in performance. While high-performance libraries often provide adequate performance within a node, distributed computing is required to scale python across nodes and make it genuinely competitive in large-scale high-performance computing. Many frameworks, such as Charm4Py, DaCe, Dask, Legate Numpy, mpi4py, and Ray, scale python across nodes. However, little is known about these frameworks' relative strengths and weaknesses, leaving practitioners and scientists without enough information about which frameworks are suitable for their requirements. In this paper, we seek to narrow this knowledge gap by studying the relative performance of two such frameworks: Charm4Py and *** perform a comparative performance analysis of Charm4Py and mpi4py using CPU and GPU-based microbenchmarks other representative mini-apps for scientific computing. © 2021 IEEE.

关键词： python

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performance of MPI Codes Written in python with NumPy and mpi4py

Performance of MPI Codes Written in Python with NumPy and mp...

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

作者： Ross Smith Aberdeen Proving Ground Engility Corporation Salt Lake City Utah USA

ISBN: (纸本)9781509052219

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.

关键词： Kernel Benchmark testing Libraries Machine learning algorithms Workstations

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Devito: Towards a Generic Finite Difference DSL Using Symbolic python

Devito: Towards a Generic Finite Difference DSL Using Symbol...

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

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

ISBN: (纸本)9781509052219

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.

关键词： DSL Kernel Arrays Optimization Ecosystems

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epython: An Implementation of python for the Many-Core Epiphany Co-processor

ePython: An Implementation of Python for the Many-Core Epiph...

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

作者： Nick Brown EPCC Edinburgh

ISBN: (纸本)9781509052219

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.

关键词： Multicore processing Coprocessors System-on-chip Parallel processing Programming profession

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