Will McLendon

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Many emerging large-scale data science applications require searching large graphs distributed across multiple memories and processors. This paper presents a distributed breadthfirst search (BFS) scheme that scales for random graphs with up to three billion vertices and 30 billion edges. Scalability was tested on IBM BlueGene/L with 32,768 nodes at the(More)
The traditional, serial, algorithm for finding the strongly connected components in a graph is based on depth first search and has complexity which is linear in the size of the graph. Depth first search is difficult to parallelize, which creates a need for a different parallel algorithm for this problem. We describe the implementation of a recently proposed(More)
<i>Discrete ordinates methods are commonly used to simulate radiation transport for fire or weapons modeling. The computation proceeds by sweeping the flux across a grid. A particular cell cannot be computed until all the cells immediately upwind of it are finished. If the directed dependence graph for the grid cells contains a cycle then sweeping methods(More)
The growth in computing resources at scientific computing centers has created new challenges for system software. These multi-teraflop systems often exceed the capabilities of the system software and require new approaches to accommodate these large processor counts. The costs associated with development and maintenance of this software are also significant(More)
The method of discrete ordinates is commonly used to solve the Boltzmann radiation transport equation for applications ranging from simulations of fires to weapons effects. The equations are most efficiently solved by sweeping the radiation flux across the computational grid. For unstructured grids this poses several interesting challenges, particularly(More)
Graph algorithms tend to suffer poor performance due to the irregularity of access patterns within general graph data structures, arising from poor data locality, which translates to high memory latency. The result is that advances in high-performance solutions for graph algorithms are most likely to come through advances in both architectures and(More)
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