Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
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Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
General-Purpose Join Algorithms for Large Graph Triangle Listing on Heterogeneous Systems
Proceedings of 9th Workshop on General-Purpose Computation on Graphics Processing Units (GPGPU-9). March 2016. 12 Mar 2016 Barcelona, Spain.
D. Zinn, H. Wu*, J. Wang*, M. Aref and S. Yalamanchili*
* Georgia Institute of Technology
We investigate applying general-purpose join algorithms to the triangle listing problem on heterogeneous systems that feature a multi-core CPU and multiple GPUs. In partic- ular, we consider an out-of-core context where graph data are available on secondary storage such as a solid-state disk (SSD) and may not fit in the CPU main memory or GPU device memory. We focus on Leapfrog Triejoin (LFTJ), a recently proposed, worst-case optimal algorithm and present “boxing”: a novel yet conceptually simple approach for par- titioning and feeding out-of-core input data to LFTJ. The “boxing” algorithm integrates well with a GPU-Optimized LFTJ algorithm for triangle listing. We achieve significant performance gains on a heterogeneous system comprised of GPUs and CPU by utilizing the massive-parallel computa- tion capability of GPUs. Our experimental evaluations on real-world and synthetic data sets (some of which containing more than 1.2 billion edges) show that out-of-core LFTJ is competitive with specialized graph algorithms for triangle listing. By using one or two GPUs, we achieve additional speedups on the same graphs.
FULL PAPER: pdf