arXiv subjects

Vitaliy Gleyzer

Publications and source records attributed to Vitaliy Gleyzer.

3 recordsLinked to original sources

arXiv2023

Exact Distributed Stochastic Block Partitioning

Stochastic block partitioning (SBP) is a community detection algorithm that is highly accurate even on graphs with a complex community structure, but its inherently serial nature hinders its widespread adoption by the wider scientific community. To make it practical to analyze large real-world graphs with SBP, there is a growing need to parallelize and distribute the algorithm. The current state-of-the-art distributed SBP algorithm is a divide-and-conquer approach that limits communication between compute nodes until the end of inference. This leads to the breaking of computational dependencies, which causes convergence issues as the number of compute nodes increases, and when the graph is sufficiently sparse. In this paper, we introduce EDiSt - an exact distributed stochastic block partitioning algorithm. Under EDiSt, compute nodes periodically share community assignments during inference. Due to this additional communication, EDiSt improves upon the divide-and-conquer algorithm by allowing it to scale out to a larger number of compute nodes without suffering from convergence issues, even on sparse graphs. We show that EDiSt provides speedups of up to 23.8X over the divide-and-conquer approach, and speedups up to 38.0X over shared memory parallel SBP when scaled out to 64 compute nodes.

cs.DC↗

arXiv2023

SamBaS: Sampling-Based Stochastic Block Partitioning

Community detection is a well-studied problem with applications in domains ranging from networking to bioinformatics. Due to the rapid growth in the volume of real-world data, there is growing interest in accelerating contemporary community detection algorithms. However, the more accurate and statistically robust methods tend to be hard to parallelize. One such method is stochastic block partitioning (SBP) - a community detection algorithm that works well on graphs with complex and heterogeneous community structure. In this paper, we present a sampling-based SBP (SamBaS) for accelerating SBP on sparse graphs. We characterize how various graph parameters affect the speedup and result quality of community detection with SamBaS and quantify the trade-offs therein. To evaluate SamBas on real-world web graphs without known ground-truth communities, we introduce partition quality score (PQS), an evaluation metric that outperforms modularity in terms of correlation with F1 score. Overall, SamBaS achieves speedups of up to 10X while maintaining result quality (and even improving result quality by over 150% on certain graphs, relative to F1 score).

cs.SI↗

arXiv2016

Novel Graph Processor Architecture, Prototype System, and Results

Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are inadequate for handling the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a rethinking of parallel architectures for graph problems. Our processor utilizes innovations that include a sparse matrix-based graph instruction set, a cacheless memory system, accelerator-based architecture, a systolic sorter, high-bandwidth multi-dimensional toroidal communication network, and randomized communications. A field-programmable gate array (FPGA) prototype of the new graph processor has been developed with significant performance enhancement over conventional processors in graph computational throughput.

cs.DC↗