Accelerating the LZ-complexity algorithm

The Lempel Ziv complexity of a string has recently been used in pattern recognition and classification as part of a string distance function. Its main advantage is that it can measure dissimilarity between a pair of strings of different lengths. This is very useful for machine learning on unstructur...

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Veröffentlicht in:Proceedings - International Conference on Parallel and Distributed Systems S. 200 - 207
Hauptverfasser: Ratsaby, Joel, Timashkov, Alexander
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 17.12.2023
Schlagworte:
Approximation algorithms
Complexity theory
CUDA
GPU
Graphics processing units
Length measurement
LZ-complexity
Machine learning
Memory management
Pattern recognition
string distance
UID distance
ISSN:2690-5965
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Zusammenfassung:The Lempel Ziv complexity of a string has recently been used in pattern recognition and classification as part of a string distance function. Its main advantage is that it can measure dissimilarity between a pair of strings of different lengths. This is very useful for machine learning on unstructured data since such data is not restricted to a fixed input dimensionality. The standard computation of LZ-complexity is inherently serial and is not suitable for processing large unstructured data. Hence, we propose a parallel algorithm that computes the LZ-complexity of strings whose length is limited only by the amount of memory, typically in the tens of gigabytes. The algorithm is implemented in CUDA on a GPU. Its speed-up factor is approximately n 2/3 for strings of length n, for at least up to n = 2Mb. For instance, on 2Mb strings, the speed-up is 150. We compare the execution times of kernel variants with shared and global memory. The more efficient variant obtains approximately 90% GPU utilization.
ISSN:2690-5965
DOI:10.1109/ICPADS60453.2023.00038