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  1. Proceedings of the 2014 International Workshop on Data Intensive Scalable Computing Systems (DISCS '14)
  2. dispel4py: a Python framework for data-intensive scientific computing
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Par-BF: a parallel partitioned Bloom filter for dynamic data sets
BPAR: a bundle-based parallel aggregation framework for decoupled I/O execution
Distributed multipath routing algorithm for data center networks
Title Page iii
dispel4py: a Python framework for data-intensive scientific computing
Rethinking key-value store for parallel I/O optimization
CULZSS-bit: a bit-vector algorithm for lossless data compression on GPGPUs
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Efficient, failure resilient transactions for parallel and distributed computing
PSA: a performance and space-aware data layout scheme for hybrid parallel file systems
A caching approach to reduce communication in graph search algorithms
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Mapping of RAID controller performance data to the job history on large computing systems

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dispel4py: a Python framework for data-intensive scientific computing

Content Provider ACM Digital Library
Author Krause, Amrey Moreno, Alexander Atkinson, Malcolm Filguiera, Rosa David, Mario Klampanos, Iraklis
Abstract This paper presents dispel4py, a new Python framework for describing abstract stream-based workflows for distributed data-intensive applications. The main aim of dispel4py is to enable scientists to focus on their computation instead of being distracted by details of the computing infrastructure they use. Therefore, special care has been taken to provide dispel4py with the ability to map abstract workflows to different enactment platforms dynamically, at run time. In this work we present four dispel4py mappings: Apache Storm, MPI, multi-threading and sequential. The results show that dispel4py is successful in enacting on different platforms, while also providing scalable performance.
Starting Page 9
Ending Page 16
Page Count 8
File Format PDF
ISBN 9781479970384
DOI 10.1109/DISCS.2014.12
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-11-16
Access Restriction Subscribed
Subject Keyword E-infrastructures Scientific workflows Data-intensive computing Programming frameworks Python Data streaming
Content Type Text
Resource Type Article
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