Fast Iterative Graph Computation with Resource Aware Graph Parallel Abstractions

Iterative computation on large graphs has challenged system research from two aspects: (1) how to conduct high per-formance parallel processing for both in-memory and out-of-core graphs; and (2) how to handle large graphs that exceed the resource boundary of traditional systems by re-source aware graph partitioning such that it is feasible to run large-scale graph analysis on a single PC. This paper presents GraphLego, a resource adaptive graph processing system with multi-level programmable graph parallel ab-stractions. GraphLego is novel in three aspects: (1) we argue that vertex-centric or edge-centric graph partitioning are ineffective for parallel processing of large graphs and we introduce three alternative graph parallel abstractions to enable a large graph to be partitioned at the granularity of subgraphs by slice, strip and dice based partitioning; (2) we use dice-based data placement algorithm to store a large graph on disk by minimizing non-sequential disk access and enabling more structured in-memory access; and (3) we dy-namically determine the right level of graph parallel abstrac-tion to maximize sequential access and minimize random access. GraphLego can run efficiently on different computers with diverse resource capacities and respond to different memory requirements by real-world graphs of different com-plexity. Extensive experiments show the competitiveness of GraphLego against existing representative graph processing systems, such as GraphChi, GraphLab and X-Stream