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PyTACC: HPC Python at the texas advanced computing center PyTACC:得克萨斯高级计算中心的HPC Python
Workshop on Python for High-Performance and Scientific Computing Pub Date : 2015-11-15 DOI: 10.1145/2835857.2835861
R. T. Evans, A. Gómez-Iglesias, W. C. Proctor
{"title":"PyTACC: HPC Python at the texas advanced computing center","authors":"R. T. Evans, A. Gómez-Iglesias, W. C. Proctor","doi":"10.1145/2835857.2835861","DOIUrl":"https://doi.org/10.1145/2835857.2835861","url":null,"abstract":"Python-based applications at the Texas Advanced Computing Center (TACC) consume a significant and growing fraction of our computational resources. To meet this demand, TACC has developed an approach to provide its users a robust, performant, and flexible Python ecosystem. HPC Centers such as TACC have unique concerns when supporting Python due to their complex system environments and diverse user base: maintenance and usage tracking of multiple Python versions/distributions/compiler-bases and associated packages, deployment of this software in a manner that is compatible with our systems and readily usable to our users, optimization of the software to maximize scientific throughout, and user support and education.","PeriodicalId":171838,"journal":{"name":"Workshop on Python for High-Performance and Scientific Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127821705","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: 2
Solving large numerical optimization problems in HPC with Python 用Python求解高性能计算中的大型数值优化问题
Workshop on Python for High-Performance and Scientific Computing Pub Date : 2015-11-15 DOI: 10.1145/2835857.2835864
A. Gómez-Iglesias
{"title":"Solving large numerical optimization problems in HPC with Python","authors":"A. Gómez-Iglesias","doi":"10.1145/2835857.2835864","DOIUrl":"https://doi.org/10.1145/2835857.2835864","url":null,"abstract":"Numerical optimization is a complex problem in which many different algorithms can be used. Distributed metaheuristics have received attention but they normally focus on small problems. Many large scientific problems can take advantage of these techniques to find optimal solutions for the problems. However, solving large scientific problems presents specific issues that traditional implementations of metaheuristics do not tackle. This research presents a large parallel optimization solver that uses Python to follow a generic model that can be easily extended with new algorithms. It also makes extensive use of NumPy for an efficient utilization of the computational resources and MPI4py for communication in HPC environments. The presented model has proven to be an excellent approach for solving very large problems in an efficient manner while using the computational resources in different HPC environments adequately.","PeriodicalId":171838,"journal":{"name":"Workshop on Python for High-Performance and Scientific Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125698155","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: 4
Feature frequency profiles for automatic sample identification using PySpark 使用PySpark进行自动样本识别的特征频率配置文件
Workshop on Python for High-Performance and Scientific Computing Pub Date : 2015-11-15 DOI: 10.1145/2835857.2835862
Gregory J. Zynda, N. Gaffney, Mehmet M. Dalkilic, M. Vaughn
{"title":"Feature frequency profiles for automatic sample identification using PySpark","authors":"Gregory J. Zynda, N. Gaffney, Mehmet M. Dalkilic, M. Vaughn","doi":"10.1145/2835857.2835862","DOIUrl":"https://doi.org/10.1145/2835857.2835862","url":null,"abstract":"When the identity of a next generation sequencing sample is lost, reads or assembled contigs are aligned to a database of known genomes and classified as the match with the most hits. However, any alignment based methods are very expensive when dealing with millions of reads and several thousand genomes with homologous sequences. Instead of relying on alignment, samples and references could be compared and classified by their feature frequency profiles (FFP), which is similar to the word frequency profile (n-gram) used to compare bodies of text. The FFP is also ideal in a metagenomics setting to reconstruct a mixed sample from a pool of reference profiles using a linear model or optimization techniques. To test the robustness of this method, an assortment of samples will be matched to complete references from NCBI Genome. Since a MapReduce framework is ideal for calculating feature frequencies in parallel, this method will be implemented using the PySpark API and run at scale on Wrangler, an XSEDE system designed for big data analytics.","PeriodicalId":171838,"journal":{"name":"Workshop on Python for High-Performance and Scientific Computing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115546712","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
PeachPy meets Opcodes: direct machine code generation from Python PeachPy满足Opcodes:从Python直接生成机器代码
Workshop on Python for High-Performance and Scientific Computing Pub Date : 2015-11-15 DOI: 10.1145/2835857.2835860
Marat Dukhan
{"title":"PeachPy meets Opcodes: direct machine code generation from Python","authors":"Marat Dukhan","doi":"10.1145/2835857.2835860","DOIUrl":"https://doi.org/10.1145/2835857.2835860","url":null,"abstract":"We introduce Opcodes, a Python package which presents x86 and x86-64 instruction sets as a set of high-level objects. Opcodes provides information about instruction names, implicit and explicit operands, and instruction encoding. We use the Opcodes package to auto-generate instruction classes for PeachPy, an x86-64 assembler embedded in Python, and enable new functionality.\u0000 The new PeachPy functionality lets low-level optimization experts write high-performance assembly kernels in Python, load them as callable Python functions, test the kernels using numpy and generate object files for Windows, Linux, and Mac OS X entirely within Python. Additionally, the new PeachPy can generate and run assembly code inside Chromium-based browsers by leveraging Native Client technology. Beyond that, PeachPy gained ability to target Google Go toolchain, by generating either source listing for Go assembler, or object files that can be linked with Go toolchain.\u0000 With backends for Windows, Linux, Mac OS X, Native Client, and Go, PeachPy is the most portable way to write high-performance kernels for x86-64 architecture.","PeriodicalId":171838,"journal":{"name":"Workshop on Python for High-Performance and Scientific Computing","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132663494","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: 4
Performance and productivity of parallel python programming: a study with a CFD test case 并行python编程的性能和生产力:CFD测试用例的研究
Workshop on Python for High-Performance and Scientific Computing Pub Date : 2015-11-15 DOI: 10.1145/2835857.2835859
A. Basermann, Melven Röhrig-Zöllner, J. Illmer
{"title":"Performance and productivity of parallel python programming: a study with a CFD test case","authors":"A. Basermann, Melven Röhrig-Zöllner, J. Illmer","doi":"10.1145/2835857.2835859","DOIUrl":"https://doi.org/10.1145/2835857.2835859","url":null,"abstract":"The programming language Python is widely used to create rapidly compact software. However, compared to low-level programming languages like C or Fortran low performance is preventing its use for HPC applications. Efficient parallel programming of multi-core systems and graphic cards is generally a complex task. Python with add-ons might provide a simple approach to program those systems. This paper evaluates the performance of Python implementations with different libraries and compares it to implementations in C or Fortran. As a test case from the field of computational fluid dynamics (CFD) a part of a rotor simulation code was selected. Fortran versions of this code were available for use on single-core, multi-core and graphic-card systems. For all these computer systems, multiple compact versions of the code were implemented in Python with different libraries. For performance analysis of the rotor simulation kernel, a performance model was developed. This model was then employed to assess the performance reached with the different implementations. Performance tests showed that an implementation with Python syntax is six times slower than Fortran on single-core systems. The performance on multi-core systems and graphic cards is about a tenth of the Fortran implementations. A higher performance was achieved by a hybrid implementation in C and Python using Cython. The latter reached about half of the performance of the Fortran implementation.","PeriodicalId":171838,"journal":{"name":"Workshop on Python for High-Performance and Scientific Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129307876","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: 1
Python, performance, and natural language processing Python、性能和自然语言处理
Workshop on Python for High-Performance and Scientific Computing Pub Date : 2015-11-15 DOI: 10.1145/2835857.2835858
Aleksandr Drozd, Anna Gladkova, S. Matsuoka
{"title":"Python, performance, and natural language processing","authors":"Aleksandr Drozd, Anna Gladkova, S. Matsuoka","doi":"10.1145/2835857.2835858","DOIUrl":"https://doi.org/10.1145/2835857.2835858","url":null,"abstract":"We present a case study of Python-based workflow for a data-intensive natural language processing problem, namely word classification with vector space model methodology. Problems in the area of natural language processing are typically solved in many steps which require transformation of the data to vastly different formats (in our case, raw text to sparse matrices to dense vectors). A Python implementation for each of these steps would require a different solution. We survey existing approaches to using Python for high-performance processing of large volumes of data, and we propose a sample solution for each step for our case study (aspectual classification of Russian verbs), attempting to preserve both efficiency and user-friendliness. For the most computationally intensive part of the workflow we develop a prototype distributed implementation of co-occurrence extraction module using IPython.parallel cluster.","PeriodicalId":171838,"journal":{"name":"Workshop on Python for High-Performance and Scientific Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131164886","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: 5
dispel4py: a Python framework for data-intensive eScience dispel4py:用于数据密集型科学的Python框架
Workshop on Python for High-Performance and Scientific Computing Pub Date : 2015-11-15 DOI: 10.1145/2835857.2835863
A. Krause, Rosa Filgueira, M. Atkinson
{"title":"dispel4py: a Python framework for data-intensive eScience","authors":"A. Krause, Rosa Filgueira, M. Atkinson","doi":"10.1145/2835857.2835863","DOIUrl":"https://doi.org/10.1145/2835857.2835863","url":null,"abstract":"We present dispel4py, a novel data intensive and high performance computing middleware provided as a standard Python library for describing stream-based workflows. 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 to MPI, 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.","PeriodicalId":171838,"journal":{"name":"Workshop on Python for High-Performance and Scientific Computing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123137899","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: 3
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