7 Matching Annotations
- Dec 2023
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superfastpython.com superfastpython.com
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It is critical to be systematic when benchmarking code.
The first step is to record how long an unmodified version of the program takes to run. This provides a baseline in performance to which all other versions of the program must be compared. If we are adding concurrency, then the unmodified version of the program will typically perform tasks sequentially, e.g. one-by-one.
The performance of the modified versions of the program must have better performance than the unmodified version of the program. If they do not, they are not improvements and should not be adopted.
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Benchmarking is the practice of comparing business processes and performance metrics to industry bests and best practices from other companies. Dimensions typically measured are quality, time and cost.
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Benchmarking Python code refers to comparing the performance of one program to variations of the program.
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- Nov 2022
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docs.google.com docs.google.com
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Devising ML Metrics
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- Sep 2022
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a benchmark tells you how slow your code is ("it took 20 seconds to do X Y Z") and a profiler tells you why it's slow ("35% of that time was spent doing compression").
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- May 2022
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github.com github.com
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before(:all) do @fiber = Fiber.new do Benchmark.ips do |benchmark| @benchmark = benchmark Fiber.yield benchmark.compare! end end @fiber.resume end
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- Dec 2020
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psyarxiv.com psyarxiv.com
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Rocca, R., & Yarkoni, T. (2020). Putting psychology to the test: Rethinking model evaluation through benchmarking and prediction. PsyArXiv. https://doi.org/10.31234/osf.io/e437b
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