Brahim Betkaoui

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In many application domains, data are represented using large graphs involving millions of vertices and billions of edges. Graph exploration algorithms, such as breadth-first search (BFS), are largely dominated by memory latency and are challenging to process efficiently. In this paper, we present a reconfigurable hardware methodology for efficient parallel(More)
This paper provides the first comparison of performance and energy efficiency of high productivity computing systems based on FPGA (Field-Programmable Gate Array) and GPU (Graphics Processing Unit) technologies. The search for higher performance compute solutions has recently led to great interest in heterogeneous systems containing FPGA and GPU(More)
In many application domains, data are represented using large graphs involving millions of vertices and edges. Graph analysis algorithms, such as finding short paths and isomorphic subgraphs, are largely dominated by memory latency. Large cluster-based computing platforms can process graphs efficiently if the graph data can be partitioned, and on a smaller(More)
This paper proposes a highly parallel and scalable reconfigurable design for the All-Pairs Shortest-Paths (APSP) algorithm for very sparse networks. Our work is motivated by a computationally intensive bioinformatics application that employs this memory-latency bound algorithm. The proposed design methodology takes advantage of distributed on-chip memory(More)
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