M-Flash: Fast Billion-scale Graph Computation Using a Bimodal Block Processing Model

TL;DR

M-Flash introduces Bimodal Block Processing to significantly accelerate billion-scale graph computations on a single machine by optimizing I/O operations and providing a flexible programming model.

Contribution

It presents M-Flash, the fastest graph computation framework with a novel bimodal block processing strategy and a simple programming model for large-scale graph algorithms.

Findings

Achieved the fastest graph computation performance to date.

Demonstrated significant speedup on real graphs with up to 6.6 billion edges.

Implemented the first single-machine billion-scale eigensolver.

Abstract

Recent graph computation approaches have demonstrated that a single PC can perform efficiently on billion-scale graphs. While these approaches achieve scalability by optimizing I/O operations, they do not fully exploit the capabilities of modern hard drives and processors. To overcome their performance, in this work, we introduce the Bimodal Block Processing (BBP), an innovation that is able to boost the graph computation by minimizing the I/O cost even further. With this strategy, we achieved the following contributions: (1) M-Flash, the fastest graph computation framework to date; (2) a flexible and simple programming model to easily implement popular and essential graph algorithms, including the first single-machine billion-scale eigensolver; and (3) extensive experiments on real graphs with up to 6.6 billion edges, demonstrating M-Flash's consistent and significant speedup.