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GraphChallenge.org triangle counting performance [e-print]

Summary

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems. GraphChallenge.org provides a wide range of preparsed graph data sets, graph generators, mathematically defined graph algorithms, example serial implementations in a variety of languages, and specific metrics for measuring performance. The triangle counting component of GraphChallenge.org tests the performance of graph processing systems to count all the triangles in a graph and exercises key graph operations found in many graph algorithms. In 2017, 2018, and 2019 many triangle counting submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art triangle counting execution time, Ttri, is a strong function of the number of edges in the graph, Ne, which improved significantly from 2017 (Ttri \approx (Ne/10^8)^4=3) to 2018 (Ttri \approx Ne/10^9) and remained comparable from 2018 to 2019. Graph Challenge provides a clear picture of current graph analysis systems and underscores the need for new innovations to achieve high performance on very large graphs
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Summary

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems...

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GraphChallenge.org: raising the bar on graph analytic performance

Summary

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems. GraphChallenge.org provides a wide range of preparsed graph data sets, graph generators, mathematically defined graph algorithms, example serial implementations in a variety of languages, and specific metrics for measuring performance. Graph Challenge 2017 received 22 submissions by 111 authors from 36 organizations. The submissions highlighted graph analytic innovations in hardware, software, algorithms, systems, and visualization. These submissions produced many comparable performance measurements that can be used for assessing the current state of the art of the field. There were numerous submissions that implemented the triangle counting challenge and resulted in over 350 distinct measurements. Analysis of these submissions show that their execution time is a strong function of the number of edges in the graph, Ne, and is typically proportional to N4=3 e for large values of Ne. Combining the model fits of the submissions presents a picture of the current state of the art of graph analysis, which is typically 108 edges processed per second for graphs with 108 edges. These results are 30 times faster than serial implementations commonly used by many graph analysts and underscore the importance of making these performance benefits available to the broader community. Graph Challenge provides a clear picture of current graph analysis systems and underscores the need for new innovations to achieve high performance on very large graphs.
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Summary

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems...

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Streaming graph challenge: stochastic block partition

Summary

An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition problem. As a combinatorial problem, graph partition is NP-hard, but existing relaxation methods provide reasonable approximate solutions that can be scaled for large graphs. Competitive benchmarks and challenges have proven to be an effective means to advance state-of-the-art performance and foster community collaboration. This paper describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity. The algorithm employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of the real-world graphs. This strong foundation enables the algorithm to address limitations of well-known graph partition approaches such as modularity maximization. This paper describes various aspects of the challenge including: (1) the data sets and streaming graph generator, (2) the baseline partition algorithm with pseudocode, (3) an argument for the correctness of parallelizing the Bayesian inference, (4) different parallel computation strategies such as node-based parallelism and matrix-based parallelism, (5) evaluation metrics for partition correctness and computational requirements, (6) preliminary timing of a Python-based demonstration code and the open source C++ code, and (7) considerations for partitioning the graph in streaming fashion. Data sets and source code for the algorithm as well as metrics, with detailed documentation are available at GraphChallenge.org.
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Summary

An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition problem. As a combinatorial problem, graph partition is NP-hard, but existing relaxation methods provide reasonable approximate solutions that can be scaled for large graphs. Competitive...

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Static graph challenge: subgraph isomorphism

Summary

The rise of graph analytic systems has created a need for ways to measure and compare the capabilities of these systems. Graph analytics present unique scalability difficulties. The machine learning, high performance computing, and visual analytics communities have wrestled with these difficulties for decades and developed methodologies for creating challenges to move these communities forward. The proposed Subgraph Isomorphism Graph Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a graph challenge that is reflective of many real-world graph analytics processing systems. The Subgraph Isomorphism Graph Challenge is a holistic specification with multiple integrated kernels that can be run together or independently. Each kernel is well defined mathematically and can be implemented in any programming environment. Subgraph isomorphism is amenable to both vertex-centric implementations and array-based implementations (e.g., using the Graph-BLAS.org standard). The computations are simple enough that performance predictions can be made based on simple computing hardware models. The surrounding kernels provide the context for each kernel that allows rigorous definition of both the input and the output for each kernel. Furthermore, since the proposed graph challenge is scalable in both problem size and hardware, it can be used to measure and quantitatively compare a wide range of present day and future systems. Serial implementations in C++, Python, Python with Pandas, Matlab, Octave, and Julia have been implemented and their single threaded performance have been measured. Specifications, data, and software are publicly available at GraphChallenge.org.
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Summary

The rise of graph analytic systems has created a need for ways to measure and compare the capabilities of these systems. Graph analytics present unique scalability difficulties. The machine learning, high performance computing, and visual analytics communities have wrestled with these difficulties for decades and developed methodologies for creating challenges...

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Novel graph processor architecture, prototype system, and results

Published in:
HPEC 2016: IEEE Conf. on High Performance Extreme Computing, 13-15 September 2016.

Summary

Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are inadequate for handling the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a rethinking of parallel architectures for graph problems. Our processor utilizes innovations that include a sparse matrix-based graph instruction set, a cacheless memory system, accelerator-based architecture, a systolic sorter, high-bandwidth multidimensional toroidal communication network, and randomized communications. A field-programmable gate array (FPGA) prototype of the new graph processor has been developed with significant performance enhancement over conventional processors in graph computational throughput.
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Summary

Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are inadequate for handling the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a rethinking of parallel architectures for graph problems. Our processor utilizes innovations that include a sparse matrix-based graph...

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Novel graph processor architecture

Published in:
Lincoln Laboratory Journal, Vol. 20, No. 1, 2013, pp. 92-104.

Summary

Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are hard-pressed to handle the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a fundamental rethinking of architectures. It utilizes innovations that include high-bandwidth three-dimensional (3D) communication links, a sparse matrix-based graph instruction set, accelerator-based architecture, a systolic sorter, randomized communications, a cacheless memory system, and 3D packaging.
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Summary

Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are hard-pressed to handle the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a fundamental rethinking of architectures. It utilizes innovations that include high-bandwidth three-dimensional (3D) communication links, a sparse matrix-based...

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Novel graph processor architecture

Published in:
Lincoln Laboratory Journal, Vol. 20, No. 1, 2013, pp. 92-104.

Summary

Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are hard-pressed to handle the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a fundamental rethinking of architectures. It utilizes innovations that include high-bandwidth three-dimensional (3D) communication links, a sparse matrix-based graph instruction set, accelerator-based architecture, a systolic sorter, randomized communications, a cacheless memory system, and 3D packaging.
READ LESS

Summary

Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are hard-pressed to handle the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a fundamental rethinking of architectures. It utilizes innovations that include high-bandwidth three-dimensional (3D) communication links, a sparse matrix-based...

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3-d graph processor

Summary

Graph algorithms are used for numerous database applications such as analysis of financial transactions, social networking patterns, and internet data. While graph algorithms can work well with moderate size databases, processors often have difficulty providing sufficient throughput when the databases are large. This is because the processor architectures are poorly matched to the graph computational flow. For example, most modern processors utilize cache based memory in order to take advantage of highly localized memory access patterns. However, memory access patterns associated with graph processing are often random in nature and can result in high cache miss rates. In addition, graph algorithms require significant overhead computation for dealing with indices of vertices and edges.
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Summary

Graph algorithms are used for numerous database applications such as analysis of financial transactions, social networking patterns, and internet data. While graph algorithms can work well with moderate size databases, processors often have difficulty providing sufficient throughput when the databases are large. This is because the processor architectures are poorly...

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