ParaGrapher: A Parallel and Distributed Graph Loading Library for Large-Scale Compressed Graphs – BigData’25 (Short Paper) | DIPSA: Data-Intensive Parallel Systems and Algorithms

DOI:

Whereas the literature describes an increasing number of graph algorithms, loading graphs remains a time-consuming component of the end-to-end execution time. Graph frameworks often rely on custom graph storage formats, that are not optimized for efficient loading of large-scale graph datasets. Furthermore, graph loading is often not optimized as it is time-consuming to implement.

This shows a demand for high-performance libraries capable of efficiently loading graphs to (i) accelerate designing new graph algorithms, (ii) to evaluate the contributions across a wide range of graph datasets, and (iii) to facilitate easy and fast comparisons across different graph frameworks.

We present ParaGrapher, a library for loading large-scale compressed graphs in parallel and distributed graph frameworks. ParaGrapher supports (a) loading the graph while the caller is blocked and (b) interleaving graph loading with graph processing. ParaGrapher is designed to support loading graphs in shared-memory, distributed-memory, and out-of-core graph processing.

We explain the design of ParaGrapher and present a performance model of graph decompression. Our evaluation shows that 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).