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18th IEEE International Conference on Software Quality, Reliability, and Security (QRS)

作者： Zhang, Peng Gao, Yuxiang Shi, Xiang SUNY Stony Brook Dept Appl Math Stony Brook NY 11794 USA Midea Emerging Technol Ctr San Jose CA 95134 USA Adv Risk & Portfolio Management ARPM New York NY 10023 USA

ISBN: (纸本)9781538677575

Quantitative Finance is a field that replies on data analysis and big data enabling software to discover market signals. In this, a decisive factor is the speed that concerns execution speed and software development speed. So, an efficient software plays a key role in helping trading firms. Inspired by this, we present a novel software: QuantCloud to integrate a parallel python system with a C++-coded Big Data system. C++ is used to implement this big data system and python is used to code the user methods. The automated parallel execution of python codes is built upon a coprocess-based parallel strategy. We test our software using two popular algorithms: moving-window and autoregressive moving-average (ARMA). We conduct an extensive comparative study between Intel Xeon E5 and Xeon Phi processors. The results show that our method achieved a nearly linear speedup for executing python codes in parallel, prefect for today's multicore processors.

关键词： Quantitative Finance Software parallel python Big Data Cloud computing

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Evaluation of Alternatives to Accelerate Scientific Numerical Calculations on Graphics Processing Units Using python 10th

Evaluation of Alternatives to Accelerate Scientific Numerica...

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10th Latin American Conference on High Performance Computing (CARLA)

作者： Villalobos, Johansell Meneses, Esteban Natl High Technol Ctr Adv Comp Lab San Jose Costa Rica Costa Rica Inst Technol Sch Comp Cartago Costa Rica

ISBN: (纸本)9783031521850;9783031521867

In this paper, the Numba, JAX, CuPy, PyTorch, and TensorFlow python GPU accelerated libraries were benchmarked using scientific numerical kernels on a NVIDIA V100 GPU. The benchmarks consisted of a simple Monte Carlo estimation, a particle interaction kernel, a stencil evolution of an array, and tensor operations. The benchmarking procedure included general memory consumption measurements, a statistical analysis of scalability with problem size to determine the best libraries for the benchmarks, and a productivity measurement using source lines of code (SLOC) as a metric. It was statistically determined that the Numba library outperforms the rest on the Monte Carlo, particle interaction, and stencil benchmarks. The deep learning libraries show better performance on tensor operations. The SLOC count was similar for all the libraries except Numba which presented a higher SLOC count which implies more time is needed for code development.

关键词： parallel Programming parallel python Graphics Processing Units

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MPI Based python Libraries for Data Science Applications

MPI Based Python Libraries for Data Science Applications

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作者： Rodgers, John Scott University of Houston

学位级别：M.S.

Tools commonly leveraged to tackle large-scale data science workflows have traditionally shied away from existing high performance computing paradigms, largely due to their lack of fault tolerance and computation resiliency. However, these concerns are typically only of critical importance to problems tackled by technology companies at the highest level. For the average data scientist, the benefits of resiliency may not be as important as the overall execution performance. To this end, the work of this thesis aims to develop prototypes of tools favored by the data science community that function in a data-parallel environment, taking advantage of functionality commonly used in high performance computing. To achieve this goal, a prototype-distributed clone of the python NumPy library and a select module from the SciPy library were developed, which leverage MPI for inter-process communication and data transfers while abstracting away the complexity of MPI programming from its users. Through various benchmarks, the overhead introduced by logic necessary to resolve functioning in a data-parallel environment, as well as the scalability of using parallel compute resources for routines commonly used by the emulated libraries, are analyzed. For the distributed NumPy clone, it was found that for routines that could act solely on their local array contents, the impact of the introduced overhead was minimal; while for routines that required global scope of distributed elements, a considerable amount of overhead was introduced. In terms of scalability, both the distributed NumPy clone and select SciPy module, a distributed implementation of K-Means clustering, exhibited reasonably performant results; notably showing sensitivity to local process problem sizes and operations that required large amounts of collective communication/synchronization. As this work mainly focused on the initial exploration and prototyping of behavior, the results of the benchmarks can be used in future

关键词： parallel python Message Passing Interface High Performance Computing Computer Science Distributed Computing

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Performance Evaluation of an AmI Testbed for Improving QoL: Evaluation Using Clustering Approach Considering Distributed Concurrent Processing 31

Performance Evaluation of an AmI Testbed for Improving QoL: ...

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31st IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA)

作者： Obukata, Ryoichiro Cuka, Miralda Elmazi, Donald Sakamoto, Shinji Oda, Tetsuya Barolli, Leonard Fukuoka Inst Technol Grad Sch Engn Higashi Ku 3-30-1 Wajiro Higashi Fukuoka 8110295 Japan Fukuoka Inst Technol Dept Informat & Commun Engn Higashi Ku 3-30-1 Wajiro Higashi Fukuoka 8110295 Japan

ISBN: (纸本)9781509062317

Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In this paper, we present the design and implementation of a testbed for AmI using Raspberry Pi mounted on Raspbian OS. We analyze the performance of k-means clustering algorithm considering sensing data. For evaluation we considered respiratory rate and heart rate metrics. We speeded up the k-means clustering algorithm by using distributed concurrent processing.

关键词： Ambient Intelligence Quality of Life Machine Learning k-means Distributed Concurrent Processing parallel python Sensing data Testbed Raspberry Pi

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Twitter Sentiment Analysis for Large-Scale Data: An Unsupervised Approach

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COGNITIVE COMPUTATION 2015年第2期7卷 254-262页

作者： Pandarachalil, Rafeeque Sendhilkumar, Selvaraju Mahalakshmi, G. S. Govt Coll Engn Kannur Dept Comp Sci & Engn Kannur India Anna Univ Dept Informat Sci & Technol Chennai 600025 Tamil Nadu India Anna Univ Dept Comp Sci & Engn Chennai 600025 Tamil Nadu India

Millions of tweets are generated each day on multifarious issues. Topical diversity in content demands domain-independent solutions for analysing twitter sentiments. Scalability is another issue when dealing with huge amount of tweets. This paper presents an unsupervised method for analysing tweet sentiments. Polarity of tweets is evaluated by using three sentiment lexicons-SenticNet, SentiWordNet and SentislangNet. SentislangNet is a sentiment lexicon built from SenticNet and SentiWordNet for slangs and acronyms. Experimental results show fairly good -score. The method is implemented and tested in parallel python framework and is shown to scale well with large volume of data on multiple cores.

关键词： Sentiment analysis Twitter SentiWordNet SenticNet parallel python

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parallel astronomical data processing with python: Recipes for multicore machines

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ASTRONOMY AND COMPUTING 2013年 2卷 1-10页

作者： Singh, Navtej Browne, Lisa-Marie Butler, Ray Natl Univ Ireland Galway Sch Phys Ctr Astron Galway Ireland

High performance computing has been used in various fields of astrophysical research. But most of it is implemented on massively parallel systems (supercomputers) or graphical processing unit clusters. With the advent of multicore processors in the last decade, many serial software codes have been re-implemented in parallel mode to utilize the full potential of these processors. In this paper, we propose parallel processing recipes for multicore machines for astronomical data processing. The target audience is astronomers who use python as their preferred scripting language and who may be using PyRAF/IRAF for data processing. Three problems of varied complexity were benchmarked on three different types of multicore processors to demonstrate the benefits, in terms of execution time, of parallelizing data processing tasks. The native multiprocessing module available in python makes it a relatively trivial task to implement the parallel code. We have also compared the three multiprocessing approaches-Pool/Map, Process/Queue and parallel python. Our test codes are freely available and can be downloaded from our website. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Astronomical data processing parallel computing Multicore programming python multiprocessing parallel python Deconvolution

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MPI for python:: Performance improvements and MPI-2 extensions

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2008年第5期68卷 655-662页

作者： Dalcin, Lisandro Paz, Rodrigo Storti, Mario D'Elia, Jorge UNL CONICET Inst Desarrollo Tecnol Ind Quim INTEC Ctr Int Metodos Computac Ing CIMEC Santa Fe Argentina

MPI for python provides bindings of the message passing interface (MPI) standard for the python programming language and allows any python program to exploit multiple processors. In its first release, NIPI for python was constructed on top of the MPI-1 specification defining an object-oriented interface that closely followed the MPI-2 C + + bindings, and provided support for communications of general python objects. In the latest release, this package is improved to enable direct blocking/non-blocking communication of numeric arrays, and to support almost all MPI-2 features. Improvements in communication performance have been tested in a Beowulf class cluster. Results showed a negligible overhead in comparison to compiled C code. MPI for python is open source and available for download on the web (http://***/). (C) 2007 Elsevier Inc. All rights reserved.

关键词： message passing MPI high-level languages parallel python

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MPI for python

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2005年第9期65卷 1108-1115页

作者： Dalcín, L Paz, R Storti, M UNL CIMEC INTEC CONICET RA-3000 Santa Fe Argentina

MPI for python provides bindings of the Message Passing Interface (MPI) standard for the python progamming language and allows any python program to exploit multiple processors. This package is constructed on top of the MPI-1 specification and defines an object-oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of general python objects. Efficiency has been tested in a Beowulf class cluster and satisfying results were obtained. MPI for python is open source and available for download on the web (http: / /www. cimec. org. ar/python). (c) 2005 Elsevier Inc. All rights reserved.

关键词： Message Passing MPI high level languages parallel python

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