2014/04/12 by Gianfranco Bilardi, Bilardi, Gianfranco, Andrea Pietracaprina +7 · 1 citation
Computer Science · #Complexity and Algorithms in Graphs #Data Structures and Algorithms (cs.DS) #Distributed #FOS: Computer and information sciences #Optimization and Search Problems #Parallel #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1404.3318
openalex publication_date 2014/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A framework is proposed for the design and analysis of network-oblivious algorithms, namely, algorithms that can run unchanged, yet efficiently, on a variety of machines characterized by different degrees of parallelism and communication capabilities. The framework prescribes that a network-oblivious algorithm be specified on a parallel model of computation where the only parameter is the problem's input size, and then evaluated on a model with two parameters, capturing parallelism granularity and communication latency. It is shown that, for a wide class of network-oblivious algorithms, optimality in the latter model implies optimality in the Decomposable BSP model, which is known to effectively describe a wide and significant class of parallel platforms. The proposed framework can be regarded as an attempt to port the notion of obliviousness, well established in the context of cache hierarchies, to the realm of parallel computation. Its effectiveness is illustrated by providing optimal network-oblivious algorithms for a number of key problems. Some limitations of the oblivious approach are also discussed.