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Modern Approaches to Profiling in Python with Scalene

Автор: Sharcnet HPC

Загружено: 2023-05-03

Просмотров: 4974

Описание:

Python is a language developers choose to write in for convenience rather than speed. However, speed can be recovered by offloading calculations to libraries which leverage lower level languages like NumPy, Cython, and more. Scalene is a high-performance CPU, GPU, and memory profiler which can illustrate where code should be passing calculations to other libraries for significant increases in speed. Scalene also includes support for Jupyter Notebooks, OpenAI suggestions for vectorizing code, as well as a significantly lower overhead and higher accuracy than other profilers. This talk will introduce the concepts required for understanding why external libraries are faster than native Python, interactions with approaches such as Cython and Just-in-Time compilers, as well as a live demonstration of Scalene on the Alliance systems inside of a Jupyter Notebook. Familiarity with Python, virtual environments, and Jupyter notebooks will be assumed.

Code and slides: https://github.com/Andesha/sharcnet-c...
_______________________________________­________

This webinar was presented by Tyler Collins (SHARCNET) on May 3rd, 2023, as a part of a series of weekly Compute Ontario Colloquia. The webinar was hosted by SHARCNET. The colloquia cover different advanced research computing (ARC) and high performance computing (HPC) topics, are approximately 45 minutes in length, and are delivered by experts in the relevant fields. Further details can be found on this web page: https://www.computeontario.ca/trainin... . Recordings, slides, and other materials can be found here: https://helpwiki.sharcnet.ca/wiki/Onl...

SHARCNET is a consortium of 19 Canadian academic institutions who share a network of high performance computers (http://www.sharcnet.ca). SHARCNET is a part of Compute Ontario (http://computeontario.ca/) and Digital Research Alliance of Canada (https://alliancecan.ca).

Modern Approaches to Profiling in Python with Scalene

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