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Haskell Data Analysis cookbook

You're reading from   Haskell Data Analysis cookbook Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783286331
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Nishant Shukla Nishant Shukla
Author Profile Icon Nishant Shukla
Nishant Shukla
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Toc

Table of Contents (14) Chapters Close

Preface 1. The Hunt for Data FREE CHAPTER 2. Integrity and Inspection 3. The Science of Words 4. Data Hashing 5. The Dance with Trees 6. Graph Fundamentals 7. Statistics and Analysis 8. Clustering and Classification 9. Parallel and Concurrent Design 10. Real-time Data 11. Visualizing Data 12. Exporting and Presenting Index

Benchmarking runtime performance in Haskell

Benchmarking runtime is the process of timing how long it takes for the code to run. We can understand whether our parallel or concurrent code is in fact faster than the naive implementation by proper benchmarking. This recipe will demonstrate how to time code runtime in Haskell.

How to do it…

  1. Import the necessary libraries as follows:
    import System.CPUTime (getCPUTime)
    import Control.Monad (replicateM_)
    import Control.Parallel.Strategies (NFData, rdeepseq)
    import Control.Exception (evaluate)
  2. Create a function to print out the duration of a pure task. Evaluate the pure expression a very large number of times (10^6), and then calculate the average CPU time it takes to run one pure task. The getCPUTime function returns the number of picoseconds since the start of the program's execution, as shown in the following code snippet:
    time :: (Num t, NFData t) => t -> IO ()
    time y = do
      let trials = 10^6
      start <- getCPUTime
      replicateM_...
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