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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...
The Graph BLAS effort to standardize a set of graph algorithms building blocks in terms of linear algebra primitives promises to deliver high performing graph algorithms and greatly impact the analysis of big data. However, there are challenges with this approach, which our data analytics miniapp miniTri exposes. In this paper, we improve upon a previously proposed task-parallel approach to linear...
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...
It is challenging to obtain scalable HPC performance on real applications, especially for data science applications with irregular memory access and computation patterns. To drive co-design efforts in architecture, system, and application design, we are developing miniapps representative of data science workloads. These in turn stress the state of the art in Graph BLAS-like Graph Algorithm Building...
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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