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Triangle counting serves as a key building block for a set of important graph algorithms in network science. In this paper, we address the IEEE HPEC Static Graph Challenge problem of triangle counting, focusing on obtaining the best parallel performance on a single multicore node. Our implementation uses a linear algebra-based approach to triangle counting that has grown out of work related to our...
Driven by the importance of relational aspects of data to decision-making, graph algorithms have been developed, based on simplified pairwise relationships, to solve a variety of problems. However, evidence has shown that hypergraphs—generalizations of graphs with (hyper)edges that connect any number of vertices—can better model complex, non-pairwise relationships in data and lead to better informed...
Numerous applications focus on the analysis of entities and the connections between them, and such data are naturally represented as graphs. In particular, the detection of a small subset of vertices with anomalous coordinated connectivity is of broad interest, for problems such as detecting strange traffic in a computer network or unknown communities in a social network. Eigenspace analysis of large-scale...
We consider how to map sparse matrices across processes to reduce communication costs in parallel sparse matrix-vector multiplication, an ubiquitous kernel in high performance computing. Our main contributions are: (i) an exact graph model for communication with general (two-dimensional) matrix distribution, and (ii) a recursive partitioning algorithm based on nested dissection that approximately...
Recent projections suggest that applications and architectures will need to attain 75 GFLOPS/W in order to support future DoD missions. Meeting this goal requires deeper understanding of kernel and application performance as a function of power and architecture. As part of the PAKCK study, a set of DoD application areas, including signal and image processing and big data/graph computation, were surveyed...
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