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  1. Proceedings of the Fourth Conference on Partitioned Global Address Space Programming Model (PGAS '10)
  2. Numerical Python for scalable architectures
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Predicting remote reuse distance patterns in UPC applications
Introducing OpenSHMEM: SHMEM for the PGAS community
Hybrid PGAS runtime support for multicore nodes
Asynchronous PGAS runtime for Myrinet networks
Unifying UPC and MPI runtimes: experience with MVAPICH
Introducing mNUMA: an extended PGAS architecture
Performance modeling for multilevel communication in SHMEM+
Improving UPC productivity via integrated development tools
X10-enabled MapReduce
Development and performance analysis of a UPC Particle-in-Cell code
XcalableMP implementation and performance of NAS Parallel Benchmarks
Extensible PGAS semantics for C++
An open-source compiler and runtime implementation for Coarray Fortran
Hiding latency in Coarray Fortran 2.0
Numerical Python for scalable architectures

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Numerical Python for scalable architectures

Content Provider ACM Digital Library
Author Kristensen, Mads Ruben Burgdorff Vinter, Brian
Abstract In this paper, we introduce DistNumPy, a library for doing numerical computation in Python that targets scalable distributed memory architectures. DistNumPy extends the NumPy module[15], which is popular for scientific programming. Replacing NumPy with Dist-NumPy enables the user to write sequential Python programs that seamlessly utilize distributed memory architectures. This feature is obtained by introducing a new backend for NumPy arrays, which distribute data amongst the nodes in a distributed memory multi-processor. All operations on this new array will seek to utilize all available processors. The array itself is distributed between multiple processors in order to support larger arrays than a single node can hold in memory. We perform three experiments of sequential Python programs running on an Ethernet based cluster of SMP-nodes with a total of 64 CPU-cores. The results show an 88% CPU utilization when running a Monte Carlo simulation, 63% CPU utilization on an N-body simulation and a more modest 50% on a Jacobi solver. The primary limitation in CPU utilization is identified as SMP limitations and not the distribution aspect. Based on the experiments we find that it is possible to obtain significant speedup from using our new array-backend without changing the original Python code.
Starting Page 1
Ending Page 9
Page Count 9
File Format PDF
ISBN 9781450304610
DOI 10.1145/2020373.2020388
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-10-12
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Productivity Parallel language Numpy
Content Type Text
Resource Type Article
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