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String Processing Algorithms - String Sorting

  • Onni Koskinen
  • Arturs Polis
  • Lari Rasku

Introduction

This repository contains pure-Python implementations of various string sorting algorithms designed for a university programming course. All algorithms were written from scratch, striving for idiomatic and easily understandable Python code over low-level or implementation-specific optimizations whenever possible. In addition, the repository contains some empirical measurements on the performance of these algorithms.

Implemented algorithms

  • MSD radix sort (radixsort.py, by Lari Rasku)
  • Burst sort (burst.py, by Onni Koskinen), plus two fallback sorting algorithms:
    • In-place multikey quicksort (mkqsort.py, by Onni Koskinen)
    • Insertion sort (insertion.py, by Onni Koskinen)
  • Multikey quicksort (quicksort.py, by Arturs Polis)
  • Ternary quicksort (quicksort.py, by Arturs Polis)

See the specified files for documentation on the algorithms. With the exception of insertion sort, all implementations were timed; the insertion sort was excepted as it is a naive implementation, lacking any optimizations for sorting sequential data. In its place, the Python builtin sorted function (using the Timsort algorithm) was timed to see how well our implementations measure against highly optimized general-purpose solutions.

Test data

The timing test data consisted of the PROTEINS, DNA and ENGLISH datasets from the Pizza&Chili Corpus, in addition to a set of URLs from Ranjan Sinha's¹ data² for his original Burstsort paper. (¹) https://sites.google.com/site/ranjansinha/home (²) http://www.cs.mu.oz.au/~rsinha/resources/data/sort.data.zip

A 100MB and a 200MB sample of each dataset was used. The ENGLISH datasets were not used as-is, but with each word split on its own line, in order to make the algorithms sort individual words and not entire lines. The statistics file documents some stringological properties of these datasets.

Timing results

Two timing result sets are included in the repository: times_11.2._16.57 and times-11.2._17.22, of which the latter is the "official" one. Though the algorithm implementations were not changed between these two runs, the former contains one failed burstsort run (marked with a ! in the leftmost column, due to a missing newline at the end of proteins.100MB) and only the userspace execution time; though we will likely ignore the real column in times-11.2._17.22, as the execution time of our algorithms is more relevant to our analysis than the time the processor spent juggling jobs or reading files. The times documented in the files are in seconds.

The pypy just-in-time compiler was used for running the tests as it performed nearly an order of magnitude better on larger datasets than standard CPython, cutting total testing time down drastically.

Running the algorithms

The Python script map.py (by Lari Rasku) provides an unified interface to our string sorting algorithms; see the file itself for further documentation.

We used the stopwatch Bash script (by Lari Rasku) to measure the performance of our algorithms; again, see the file itself for exact documentation. The testset file documents our exact stopwatch testing configuration.