On the Architectural Requirements for Efficient Execution of Graph Algorithms

Bader, David A.; Cong, Guojing; Feo, John

doi:10.1109/icpp.2005.55

Cited by 70 publications

(47 citation statements)

References 36 publications

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“…Searching large graphs poses difficult challenges, because the potentially vast data set is combined with the lack of spatial and temporal locality in the access pattern. In fact, few parallel algorithms outperform their best sequential implementations on clusters due to long memory latencies and high synchronization costs [3]. Additionally, these difficulties call for more attention if dealing with commodity multicore architectures because of the complexity of their memory hierarchy and cache-coherence protocols.…”

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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“…In prior work, we have designed novel parallel algorithms for several graph problems that run efficiently on shared memory systems. Our implementations of breadth-first graph traversal [8], shortest paths [32,17], spanning tree [5], MST, connected components [6], and other problems achieve impressive parallel speedup for arbitrary, sparse graph instances. We redesign and integrate several of our recent parallel graph algorithms into SNAP, with additional optimizations for small-world networks.…”

Section: The Snap Infrastructure For Exploratory Network Analysismentioning

confidence: 99%

“…5 Mark edge e m as deleted in the graph G. 6 Run connected components on G, update dendrogram and number of clusters in parallel. 7 Compute modularity of the current partitioning in parallel.…”

Section: Algorithm 1: Approximate Betweenness-based Divisive Algorithmentioning

confidence: 99%

SNAP, Small-world Network Analysis and Partitioning: An open-source parallel graph framework for the exploration of large-scale networks

Bader

Madduri

2008

2008 IEEE International Symposium on Parallel and Distributed Processing

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“…Although it is cognizant of the greater cost of heavily multithreaded systems, it argues they are better for graph algorithms due to their memory latency tolerance and support for fine-grained dynamic threading. Bader et al [4] also endorse heavily threaded systems because of concerns of memory accesses being mostly non-contiguous (low locality).…”

Section: Related Workmentioning

confidence: 99%

Locality Exists in Graph Processing: Workload Characterization on an Ivy Bridge Server

Beamer

Asanović

Patterson

2015

2015 IEEE International Symposium on Workload Characterization

145

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Abstract-Graph processing is an increasingly important application domain and is typically communication-bound. In this work, we analyze the performance characteristics of three highperformance graph algorithm codebases using hardware performance counters on a conventional dual-socket server. Unlike many other communication-bound workloads, graph algorithms struggle to fully utilize the platform's memory bandwidth and so increasing memory bandwidth utilization could be just as effective as decreasing communication. Based on our observations of simultaneous low compute and bandwidth utilization, we find there is substantial room for a different processor architecture to improve performance without requiring a new memory system.

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On the Architectural Requirements for Efficient Execution of Graph Algorithms

Abstract: Combinatorial problems such as those from graph theory pose serious challenges for parallel machines due to non-contiguous, concurrent

Cited by 70 publications

References 36 publications

Scalable Graph Exploration on Multicore Processors

Scalable Graph Exploration on Multicore Processors

SNAP, Small-world Network Analysis and Partitioning: An open-source parallel graph framework for the exploration of large-scale networks

Locality Exists in Graph Processing: Workload Characterization on an Ivy Bridge Server

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