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We introduce an extension to the CLRS algorithmic learning benchmark,prioritizing scalability and the utilization of sparse representations. Manyalgorithms in CLRS require global memory or information exchange, mirrored inits execution model, which constructs fully connected (not sparse) graphs basedon the underlying problem. Despite CLRS's aim of assessing how effectivelylearned algorithms can generalize to larger instances, the existing executionmodel becomes a significant constraint due to its demanding memory requirementsand runtime (hard to scale). However, many important algorithms do not demand afully connected graph; these algorithms, primarily distributed in nature, alignclosely with the message-passing paradigm employed by Graph Neural Networks.Hence, we propose SALSA-CLRS, an extension of the current CLRS benchmarkspecifically with scalability and sparseness in mind. Our approach includesadapted algorithms from the original CLRS benchmark and introduces new problemsfrom distributed and randomized algorithms. Moreover, we perform a thoroughempirical evaluation of our benchmark. Code is publicly available athttps://github.com/jkminder/SALSA-CLRS.

SALSA-CLRS: A Sparse and Scalable Benchmark for Algorithmic Reasoning