2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC)最新文献

ePython: An Implementation of Python for the Many-Core Epiphany Co-processor Python:用于多核Epiphany协处理器的Python实现

2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC) Pub Date : 2016-11-13 DOI: 10.1109/PyHPC.2016.012

Nick Brown

{"title":"ePython: An Implementation of Python for the Many-Core Epiphany Co-processor","authors":"Nick Brown","doi":"10.1109/PyHPC.2016.012","DOIUrl":"https://doi.org/10.1109/PyHPC.2016.012","url":null,"abstract":"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 tools.In 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.","PeriodicalId":178771,"journal":{"name":"2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128559380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

High-Performance Python-C++ Bindings with PyPy and Cling 带有PyPy和Cling的高性能python - c++绑定

2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC) Pub Date : 2016-11-13 DOI: 10.5555/3019083.3019087

W. Lavrijsen, Aditi Dutta

{"title":"High-Performance Python-C++ Bindings with PyPy and Cling","authors":"W. Lavrijsen, Aditi Dutta","doi":"10.5555/3019083.3019087","DOIUrl":"https://doi.org/10.5555/3019083.3019087","url":null,"abstract":"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.","PeriodicalId":178771,"journal":{"name":"2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130493288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Boosting Python Performance on Intel Processors: A Case Study of Optimizing Music Recognition 提升Python在Intel处理器上的性能:优化音乐识别的案例研究

2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC) Pub Date : 2016-11-13 DOI: 10.1109/PYHPC.2016.7

Yuanzhe Li, L. Schwiebert

引用次数: 1

Performance of MPI Codes Written in Python with NumPy and mpi4py 用NumPy和mpi4py用Python编写的MPI代码的性能

2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC) Pub Date : 2016-11-13 DOI: 10.1109/PYHPC.2016.6

Ross Smith

引用次数: 11

Dynamic Provisioning and Execution of HPC Workflows Using Python 用Python实现HPC工作流的动态配置和执行

2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC) Pub Date : 2016-11-13 DOI: 10.1109/PYHPC.2016.11

Chris Harris, P. O’leary, M. Grauer, Aashish Chaudhary, Chris Kotfila, Robert M. O'Bara

引用次数: 3

Mrs: High Performance MapReduce for Iterative and Asynchronous Algorithms in Python Mrs:高性能MapReduce迭代和异步算法在Python

2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC) Pub Date : 2016-11-13 DOI: 10.1109/PYHPC.2016.10

Jeffrey Lund, C. Ashcraft, Andrew W. McNabb, Kevin Seppi

{"title":"Mrs: High Performance MapReduce for Iterative and Asynchronous Algorithms in Python","authors":"Jeffrey Lund, C. Ashcraft, Andrew W. McNabb, Kevin Seppi","doi":"10.1109/PYHPC.2016.10","DOIUrl":"https://doi.org/10.1109/PYHPC.2016.10","url":null,"abstract":"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 well.Therefore, 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 MapReduce.We then demonstrate Mrs' enhanced performance in the context of two iterative applications: particle swarm optimization (PSO), and expectation maximization (EM).","PeriodicalId":178771,"journal":{"name":"2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128262122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PALLADIO: A Parallel Framework for Robust Variable Selection in High-Dimensional Data 高维数据鲁棒变量选择的并行框架

2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC) Pub Date : 2016-11-13 DOI: 10.1109/PYHPC.2016.13

Matteo Barbieri, Samuele Fiorini, Federico Tomasi, A. Barla

引用次数: 7

Migrating Legacy Fortran to Python While Retaining Fortran-Level Performance through Transpilation and Type Hints 将传统的Fortran迁移到Python，同时通过编译和类型提示保留Fortran级别的性能

2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC) Pub Date : 2016-11-13 DOI: 10.1109/PYHPC.2016.12

Mateusz Bysiek, Aleksandr Drozd, S. Matsuoka

引用次数: 10

A New Architecture for Optimization Modeling Frameworks 一种优化建模框架的新体系结构

2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC) Pub Date : 2016-09-12 DOI: 10.1109/PYHPC.2016.5

Matt Wytock, Steven Diamond, Felix Heide, Stephen P. Boyd

引用次数: 7

Devito: Towards a Generic Finite Difference DSL Using Symbolic Python Devito:用符号Python实现一般有限差分DSL

2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC) Pub Date : 2016-09-12 DOI: 10.1109/PYHPC.2016.9

Michael Lange, Navjot Kukreja, M. Louboutin, F. Luporini, Felippe Vieira, Vincenzo Pandolfo, Paulius Velesko, Paulius Kazakas, G. Gorman

引用次数: 32