Introduction to Deep Dive On Codeglass For Julia Profiling
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Deep Dive On Codeglass For Julia Profiling Comprehensive Overview
Understanding the performance of parallel code is tricky, however In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. This video demonstrates interactive tools for exploring code and diagnosing why some code runs slowly due to "type instability."
Tricks.jl is a package that does cool tricks to do more work at compile time. It does this by generating (`@generated`) functions that ...
Summary & Highlights for Deep Dive On Codeglass For Julia Profiling
- During this workshop I will explain the design of indexing of the DataFrame type provided by the DataFrames.jl package. Next a ...
- In this intermediate-level
- It kinda feels like how design operates at Anthropic is consistently 3-6 months ahead of the rest of the industry. As a result ...
- Other SFU Research Computing training events: https://training.westdri.ca/blog Training Inquiries: training@westdri.ca.
- Dockerfiles for
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