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  1. Proceedings of the 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC '16)
  2. Boosting Python performance on Intel processors: a case study of optimizing music recognition
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Dynamic provisioning and execution of HPC workflows using Python
Migrating legacy Fortran to Python while retaining Fortran-level performance through transpilation and type hints
PALLADIO: a parallel framework for robust variable selection in high-dimensional data
High-performance Python-C++ bindings with PyPy and Cling
A new architecture for optimization modeling frameworks
Performance of MPI codes written in Python with NumPy and mpi4py
Boosting Python performance on Intel processors: a case study of optimizing music recognition
ePython: an implementation of Python for the many-core Epiphany coprocessor
Devito: towards a generic finite difference DSL using symbolic Python
Mrs: high performance mapreduce for iterative and asynchronous algorithms in Python

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Boosting Python performance on Intel processors: a case study of optimizing music recognition

Content Provider ACM Digital Library
Author Schwiebert, Loren Li, Yuanzhe
Abstract We present a case study of optimizing a Python-based music recognition application on Intel Haswell Xeon processor. With support from Numpy and Scipy, Python addresses the requirements of the music recognition problem with math library utilization and special structures for data access. However, a general optimized Python application cannot fully utilize the latest high performance multicore processors. In this study, we survey an existing open-source music recognition application, written in Python, to explore the effect of applying changes to the Scipy and Numpy libraries to achieve full processor resource occupancy and reduce code latency. Instead of comparing across many different architectures, we focus on Intel high performance processors that have multiple cores and vector registers, and we attempt to preserve both user-friendliness and code scalability so that the revised library functions can be ported to other platforms and require no additional code changes.
Starting Page 52
Ending Page 58
Page Count 7
ISBN 9781509052202
DOI 10.1109/PyHPC.2016.7
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-11-13
Access Restriction Subscribed
Subject Keyword Python performance optimization Vector processing Roofline
Content Type Text
Resource Type Article
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