SALSA-CLRS: A Sparse and Scalable Benchmark for Algorithmic Reasoning
We introduce an extension to the CLRS algorithmic learning benchmark, prioritizing scalability and the utilization of sparse representations. Many algorithms in CLRS require global memory or information exchange, mirrored in its execution model, which constructs fully connected (not sparse) graphs b...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
21.09.2023
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Subjects | |
Online Access | Get full text |
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Summary: | We introduce an extension to the CLRS algorithmic learning benchmark,
prioritizing scalability and the utilization of sparse representations. Many
algorithms in CLRS require global memory or information exchange, mirrored in
its execution model, which constructs fully connected (not sparse) graphs based
on the underlying problem. Despite CLRS's aim of assessing how effectively
learned algorithms can generalize to larger instances, the existing execution
model becomes a significant constraint due to its demanding memory requirements
and runtime (hard to scale). However, many important algorithms do not demand a
fully connected graph; these algorithms, primarily distributed in nature, align
closely with the message-passing paradigm employed by Graph Neural Networks.
Hence, we propose SALSA-CLRS, an extension of the current CLRS benchmark
specifically with scalability and sparseness in mind. Our approach includes
adapted algorithms from the original CLRS benchmark and introduces new problems
from distributed and randomized algorithms. Moreover, we perform a thorough
empirical evaluation of our benchmark. Code is publicly available at
https://github.com/jkminder/SALSA-CLRS. |
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DOI: | 10.48550/arxiv.2309.12253 |