Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing最新文献

GPUMap: A Transparently GPU-accelerated Python Map Function GPUMap:一个透明的gpu加速Python Map函数

Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing Pub Date : 2017-11-12 DOI: 10.1145/3149869.3149875

I. Pachev, C. Lupo

引用次数: 1

Real-Time Financial Risk Measurement of Dynamic Complex Portfolios with Python and PyOpenCL 基于Python和PyOpenCL的动态复杂投资组合的实时财务风险度量

Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing Pub Date : 2017-11-12 DOI: 10.1145/3149869.3149872

J. Varela, N. Wehn, Sascha Desmettre, R. Korn

{"title":"Real-Time Financial Risk Measurement of Dynamic Complex Portfolios with Python and PyOpenCL","authors":"J. Varela, N. Wehn, Sascha Desmettre, R. Korn","doi":"10.1145/3149869.3149872","DOIUrl":"https://doi.org/10.1145/3149869.3149872","url":null,"abstract":"Risk measures, such as value-at-risk and expected shortfall, are widely used to keep track of the risk at which a financial portfolio is exposed. This analysis is not only a key part of the daily operation of financial institutions worldwide, but it is also strictly enforced by regulators. While nested Monte Carlo simulations are the most flexible approach that can even deal with portfolios containing complicated derivatives, they traditionally suffer from a high computational complexity. This limits their application at certain intervals of time, mostly daily, by temporarily keeping the composition of the portfolio static. In this work, we bring together for the first time nested Monte Carlo simulations with the real-time continuous risk measurement of complex portfolios that dynamically change their composition during intraday operation. By combining the development productivity offered by Python, state-of-the-art mathematical optimizations, and the high performance capabilities offered by PyOpenCL targeting heterogeneous computing systems, our new approach reaches a throughput between 16 and 191 trading orders per second per computing node, which corresponds to the worst-case and best-case scenarios respectively. We have also made use of the Jupyter Notebook, as an interactive interface in an interdisciplinary research environment.","PeriodicalId":223702,"journal":{"name":"Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116568882","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

Real-time Thermal Medium-based Breathing Analysis with Python 基于热介质的实时呼吸分析

Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing Pub Date : 2017-11-12 DOI: 10.1145/3149869.3149874

Breawn Schoun, S. Transue, Min-Hyung Choi

{"title":"Real-time Thermal Medium-based Breathing Analysis with Python","authors":"Breawn Schoun, S. Transue, Min-Hyung Choi","doi":"10.1145/3149869.3149874","DOIUrl":"https://doi.org/10.1145/3149869.3149874","url":null,"abstract":"Respiration monitoring is an important physiological measurement taken to determine the health of an individual. In clinical sleep studies, respiration activity is monitored to detect sleep disorders such as sleep apnea and respiratory conditions such as Chronic Obstructive Pulmonary Disease (COPD). Existing methods of respiration monitoring either place sensors on the patient's body, causing discomfort to the patient, or monitor respiration remotely with lower accuracy. We present a method of respiratory analysis that is non-contact, but also measures the exhaled air of a human subject directly through a medium-based exhale visualization technique. In this method, we place a thin medium perpendicular to the exhaled airflow of an individual, and use a thermal camera to record the heat signature from the exhaled breath on the opposite side of the material. Respiratory behaviors are extracted from the thermal data in real time using Python. Our prototype is an embedded, low-power device that performs image and signal processing in realtime with Python, making use of powerful existing Python modules for scientific computing and visualization. Our proposed respiration monitoring technique accurately reports breathing rate, and may provide other metrics not obtainable through other non-contact methods. This method can be useful for medical applications where long-term respiratory analysis is necessary, and for applications that require additional information about breathing behavior.","PeriodicalId":223702,"journal":{"name":"Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115431108","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: 9

Enabling Python to execute efficiently in heterogeneous distributed infrastructures with PyCOMPSs 使用pycomps使Python能够在异构分布式基础架构中高效执行

Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing Pub Date : 2017-11-12 DOI: 10.1145/3149869.3149870

R. Amela, Cristian Ramon-Cortes, J. Ejarque, Javier Conejero, Rosa M. Badia

引用次数: 9

nbodykit: A Python Toolkit for Cosmology Simulations and Data Analysis on Parallel HPC Systems nbodykit:用于并行HPC系统上的宇宙学模拟和数据分析的Python工具包

Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing Pub Date : 2017-11-12 DOI: 10.1145/3149869.3149876

N. Hand, Yu Feng

引用次数: 3

Python and HPC for High Energy Physics Data Analyses 用于高能物理数据分析的Python和HPC

Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing Pub Date : 2017-11-12 DOI: 10.1145/3149869.3149877

S. Sehrish, J. Kowalkowski, M. Paterno, C. Green

{"title":"Python and HPC for High Energy Physics Data Analyses","authors":"S. Sehrish, J. Kowalkowski, M. Paterno, C. Green","doi":"10.1145/3149869.3149877","DOIUrl":"https://doi.org/10.1145/3149869.3149877","url":null,"abstract":"High level abstractions in Python that can utilize computing hardware well seem to be an attractive option for writing data reduction and analysis tasks. In this paper, we explore the features available in Python which are useful and efficient for end user analysis in High Energy Physics (HEP). A typical vertical slice of an HEP data analysis is somewhat fragmented: the state of the reduction/analysis process must be saved at certain stages to allow for selective reprocessing of only parts of a generally time-consuming workflow. Also, algorithms tend to to be modular because of the heterogeneous nature of most detectors and the need to analyze different parts of the detector separately before combining the information. This fragmentation causes difficulties for interactive data analysis, and as data sets increase in size and complexity (O10 TiB for a \"small\" neutrino experiment to the O10 PiB currently held by the CMS experiment at the LHC), data analysis methods traditional to the field must evolve to make optimum use of emerging HPC technologies and platforms. Mainstream big data tools, while suggesting a direction in terms of what can be done if an entire data set can be available across a system and analysed with high-level programming abstractions, are not designed with either scientific computing generally, or modern HPC platform features in particular, such as data caching levels, in mind. Our example HPC use case is a search for a new elementary particle which might explain the phenomenon known as \"Dark Matter\". Using data from the CMS detector, we will use HDF5 as our input data format, and MPI with Python to implement our use case.","PeriodicalId":223702,"journal":{"name":"Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123808386","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: 7

Python in the NERSC Exascale Science Applications Program for Data 在NERSC百亿亿次数据科学应用程序中的Python

Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing Pub Date : 2017-11-12 DOI: 10.1145/3149869.3149873

Z. Ronaghi, R. Thomas, J. Deslippe, Stephen Bailey, D. Gürsoy, T. Kisner, R. Keskitalo, J. Borrill

{"title":"Python in the NERSC Exascale Science Applications Program for Data","authors":"Z. Ronaghi, R. Thomas, J. Deslippe, Stephen Bailey, D. Gürsoy, T. Kisner, R. Keskitalo, J. Borrill","doi":"10.1145/3149869.3149873","DOIUrl":"https://doi.org/10.1145/3149869.3149873","url":null,"abstract":"We describe a new effort at the National Energy Research Scientific Computing Center (NERSC) in performance analysis and optimization of scientific Python applications targeting the Intel Xeon Phi (Knights Landing, KNL) manycore architecture. The Python-centered work outlined here is part of a larger effort called the NERSC Exascale Science Applications Program (NESAP) for Data. NESAP for Data focuses on applications that process and analyze high-volume, high-velocity data sets from experimental or observational science (EOS) facilities supported by the US Department of Energy Office of Science. We present three case study applications from NESAP for Data that use Python. These codes vary in terms of \"Python purity\" from applications developed in pure Python to ones that use Python mainly as a convenience layer for scientists without expertise in lower level programming languages like C, C++ or Fortran. The science case, requirements, constraints, algorithms, and initial performance optimizations for each code are discussed. Our goal with this paper is to contribute to the larger conversation around the role of Python in high-performance computing today and tomorrow, highlighting areas for future work and emerging best practices.","PeriodicalId":223702,"journal":{"name":"Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing","volume":"86 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123784320","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: 8

Efficient Pattern Matching in Python Python中的高效模式匹配

Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing Pub Date : 2017-09-29 DOI: 10.1145/3149869.3149871

Manuel Krebber, Henrik Barthels, P. Bientinesi

引用次数: 7

Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing 第七届高性能和科学计算Python研讨会论文集

Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing Pub Date : 2016-11-13 DOI: 10.1145/3149869

A. Schreiber, W. Scullin, Bill Spotz, A. Terrel

{"title":"Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing","authors":"A. Schreiber, W. Scullin, Bill Spotz, A. Terrel","doi":"10.1145/3149869","DOIUrl":"https://doi.org/10.1145/3149869","url":null,"abstract":"The high-level programming language Python is well established with a large community in academia and industry. It is a general-purpose language adopted by many scientific applications. Examples are computational fluid dynamics, bio molecular simulation, machine learning, data analysis, or scientific visualization. Scientists, engineers, and educators use Python for scientific computing, high-performance computing, and distributed computing. Traditionally, system administrators use Python for system management and automating administration tasks. Python is extremely easy to learn due to its very clean syntax and great readability. Therefore developers love Python as it facilitates to write sustainable and maintainable software systems. For the same reasons, Python is well suited for education at all levels. The overarching theme of the workshop is productivity vs. performance in HPC and scientific programming. While Python is extremely strong in supporting human productivity, it still lacks in computational performance compared to 'traditional' HPC languages such as Fortran or C. The workshop brings together researchers and practitioners using Python in all aspects of high performance and scientific computing. The aim is to present Python applications from mathematics, science, and engineering, to discuss general topics regarding the use of Python, and to share experiences using Python in scientific computing education.","PeriodicalId":223702,"journal":{"name":"Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing","volume":"11 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":"132541051","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