2024/04/30 by Mohsen Koohi Esfahani, Marco D’Antonio, Esfahani, Mohsen Koohi +7 · 1 citation
Computer Science · #Computational Geometry and Mesh Generation #Digital Image Processing Techniques #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Interactive and Immersive Displays #Performance (cs.PF) #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2404.19735
openalex publication_date 2024/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 explain the design of ParaGrapher and present a performance model of graph decompression, which is used for evaluation of ParaGrapher over three storage types. Our evaluation shows that by decompressing compressed graphs in WebGraph format, ParaGrapher delivers up to 3.2 times speedup in loading and up to 5.2 times speedup in end-to-end execution (i.e., through interleaved loading and execution) in comparison to the binary and textual formats. ParaGrapher is available online on https://blogs.qub.ac.uk/DIPSA/ParaGrapher/.