Efficient Breadth-First Search on the Cell/BE Processor

Scarpazza, Daniele Paolo; Villa, Oreste; Petrini, Fabrizio

doi:10.1109/tpds.2007.70811

Cited by 53 publications

(29 citation statements)

References 41 publications

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“…A good amount of literature deals with the design of BFS solutions, either based on commodity processors [11], [12] or special purpose hardware [13], [14], [15], [16]. Some recent publications describe successful parallelization strategies of list ranking [17] and phylogenetic trees on the Cell BE [18].…”

Section: Introductionmentioning

confidence: 99%

Scalable Graph Exploration on Multicore Processors

Agarwal

Petrini

Pasetto³

et al. 2010

2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis

194

151

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Abstract-Many important problems in computational sciences, social network analysis, security, and business analytics, are data-intensive and lend themselves to graph-theoretical analyses. In this paper we investigate the challenges involved in exploring very large graphs by designing a breadth-first search (BFS) algorithm for advanced multi-core processors that are likely to become the building blocks of future exascale systems. Our new methodology for large-scale graph analytics combines a highlevel algorithmic design that captures the machine-independent aspects, to guarantee portability with performance to future processors, with an implementation that embeds processorspecific optimizations. We present an experimental study that uses state-of-the-art Intel Nehalem EP and EX processors and up to 64 threads in a single system. Our performance on several benchmark problems representative of the power-law graphs found in real-world problems reaches processing rates that are competitive with supercomputing results in the recent literature. In the experimental evaluation we prove that our graph exploration algorithm running on a 4-socket Nehalem EX is (1) 2.4 times faster than a Cray XMT with 128 processors when exploring a random graph with 64 million vertices and 512 millions edges, (2) capable of processing 550 million edges per second with an R-MAT graph with 200 million vertices and 1 billion edges, comparable to the performance of a similar graph on a Cray MTA-2 with 40 processors and (3) 5 times faster than 256 BlueGene/L processors on a graph with average degree 50.

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Section: Introductionmentioning

confidence: 99%

Scalable Graph Exploration on Multicore Processors

Agarwal

Petrini

Pasetto³

et al. 2010

2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis

194

151

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“…Scarpazza et al [26] describe a similar hybrid variation for the Cell BE processor architecture. Instead of separate contraction phase per iteration, processor cores perform edge expansion, exchange, and contraction in batches.…”

Section: Parallel Breadth-first Searchmentioning

confidence: 99%

“…Our strategy for status-lookup incorporates a bitmask to reduce the size of status data from a 32-bit label to a single bit per vertex. CPU parallelizations have used atomicallyupdated bitmask structures to reduce memory traffic via improved cache coverage [5], [26]. Because we avoid atomic operations, our bitmask is only a conservative approximation of visitation status.…”

Section: Coupling Of Gathering and Lookupmentioning

confidence: 99%

High-Performance and Scalable GPU Graph Traversal

Merrill

Garland

Grimshaw

2015

ACM Trans. Parallel Comput.

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Breadth-first search (BFS) is a core primitive for graph traversal and a basis for many higher-level graph analysis algorithms. It is also representative of a class of parallel computations whose memory accesses and work distribution are both irregular and data-dependent. Recent work has demonstrated the plausibility of GPU sparse graph traversal, but has tended to focus on asymptotically inefficient algorithms that perform poorly on graphs with non-trivial diameter.We present a BFS parallelization focused on fine-grained task management that achieves an asymptotically optimal O(|V|+|E|) work complexity. Our implementation delivers excellent performance on diverse graphs, achieving traversal rates in excess of 3.3 billion and 8.3 billion traversed edges per second using single and quad-GPU configurations, respectively. This level of performance is several times faster than state-of-the-art implementations both CPU and GPU platforms.

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“…The BFS implementation of Scarpazza et al [32] for the Cell/B.E. processor, while being a multicore implementation, shares a lot of similarities with the general "explicit partitioning and edge aggregation" BFS strategy for distributed memory system.…”

Section: Parallel Bfs: Prior Workmentioning

confidence: 99%

Parallel Breadth-First Search on Distributed Memory Systems

Buluç¹,

Madduri²

2011

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Data-intensive, graph-based computations are pervasive in several scientific applications, and are known to to be quite challenging to implement on distributed memory systems. In this work, we explore the design space of parallel algorithms for Breadth-First Search (BFS), a key subroutine in several graph algorithms. We present two highly-tuned parallel approaches for BFS on large parallel systems: a levelsynchronous strategy that relies on a simple vertex-based partitioning of the graph, and a two-dimensional sparse matrixpartitioning-based approach that mitigates parallel communication overhead. For both approaches, we also present hybrid versions with intra-node multithreading. Our novel hybrid two-dimensional algorithm reduces communication times by up to a factor of 3.5, relative to a common vertex based approach. Our experimental study identifies execution regimes in which these approaches will be competitive, and we demonstrate extremely high performance on leading distributed-memory parallel systems. For instance, for a 40,000-core parallel execution on Hopper, an AMD MagnyCours based system, we achieve a BFS performance rate of 17.8 billion edge visits per second on an undirected graph of 4.3 billion vertices and 68.7 billion edges with skewed degree distribution.

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Efficient Breadth-First Search on the Cell/BE Processor

Cited by 53 publications

References 41 publications

Scalable Graph Exploration on Multicore Processors

Scalable Graph Exploration on Multicore Processors

High-Performance and Scalable GPU Graph Traversal

Parallel Breadth-First Search on Distributed Memory Systems

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