Selective Parallel Loading of Large-Scale Compressed Graphs with ParaGrapher
Mohsen Koohi Esfahani, Marco D'Antonio, Syed Ibtisam Tauhidi, Thai Son Mai, Hans Vandierendonck

TL;DR
ParaGrapher is a high-performance library that efficiently loads large-scale compressed graphs, significantly speeding up graph processing tasks and enabling comprehensive evaluation across different frameworks.
Contribution
It introduces ParaGrapher, a novel API and library supporting fast loading of large-scale compressed graphs across various processing environments.
Findings
Up to 3.2x faster graph loading with WebGraph format.
Up to 5.2x speedup in end-to-end graph processing.
Effective performance model for graph decompression.
Abstract
Comprehensive evaluation is one of the basis of experimental science. In High-Performance Graph Processing, a thorough evaluation of contributions becomes more achievable by supporting common input formats over different frameworks. However, each framework creates its specific format, which may not support reading large-scale real-world graph datasets. This shows a demand for high-performance libraries capable of loading graphs to (i) accelerate designing new graph algorithms, (ii) to evaluate the contributions on a wide range of graph algorithms, and (iii) to facilitate easy and fast comparison over different graph frameworks. To that end, we present ParaGrapher, a high-performance API and library for loading large-scale and compressed graphs. ParaGrapher supports different types of requests for accessing graphs in shared- and distributed-memory and out-of-core graph processing. We…
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Taxonomy
TopicsComputational Geometry and Mesh Generation · Interactive and Immersive Displays · Digital Image Processing Techniques
