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The Combinatorial BLAS: Design, Implementation, and Applications
, 2010
"... This paper presents a scalable highperformance software library to be used for graph analysis and data mining. Large combinatorial graphs appear in many applications of highperformance computing, including computational biology, informatics, analytics, web search, dynamical systems, and sparse mat ..."
Abstract

Cited by 58 (10 self)
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This paper presents a scalable highperformance software library to be used for graph analysis and data mining. Large combinatorial graphs appear in many applications of highperformance computing, including computational biology, informatics, analytics, web search, dynamical systems, and sparse matrix methods. Graph computations are difficult to parallelize using traditional approaches due to their irregular nature and low operational intensity. Many graph computations, however, contain sufficient coarse grained parallelism for thousands of processors, which can be uncovered by using the right primitives. We describe the Parallel Combinatorial BLAS, which consists of a small but powerful set of linear algebra primitives specifically targeting graph and data mining applications. We provide an extendible library interface and some guiding principles for future development. The library is evaluated using two important graph algorithms, in terms of both performance and easeofuse. The scalability and raw performance of the example applications, using the combinatorial BLAS, are unprecedented on distributed memory clusters.
Brief Announcement: Scalability versus Semantics of Concurrent FIFO Queues ∗
"... Maintaining data structure semantics of concurrent queues such as firstin firstout (FIFO) ordering requires expensive synchronization mechanisms which limit scalability. However, deviating from the original semantics of a given data structure may allow for a higher degree of scalability and yet be ..."
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Cited by 6 (5 self)
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Maintaining data structure semantics of concurrent queues such as firstin firstout (FIFO) ordering requires expensive synchronization mechanisms which limit scalability. However, deviating from the original semantics of a given data structure may allow for a higher degree of scalability and yet be tolerated by many concurrent applications. We introduce the notion of a kFIFO queue which may be out of FIFO order up to a constant k (called semantical deviation). Implementations of kFIFO queues may be distributed and therefore be accessed unsynchronized while still being starvationfree. We show that kFIFO queues whose implementations are based on stateoftheart FIFO queues, which typically do not scale under high contention, provide scalability. Moreover, probabilistic versions of kFIFO queues improve scalability further but only bound semantical deviation with high probability.