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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.

Boosting Python Performance on Intel Processors: A Case Study of Optimizing Music Recognition.