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

Beamer, Scott; Asanović, Krste; Patterson, David A.

doi:10.1109/iiswc.2015.12

Cited by 144 publications

(87 citation statements)

References 38 publications

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“…Graph500 was introduced in 2010 as a benchmark for data intensive applications. Beamer et al characterize the memory requirements and locality of the benchmark, but they do not study the impact of the network and its utilization. Previous works from Anghel et al and Fuentes et al provide a comprehensive characterization of the communications in the simple implementation of the benchmark.…”

Section: Related Workmentioning

confidence: 99%

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A scalable synthetic traffic model of Graph500 for computer networks analysis

Fuentes

Benito

Vallejo

et al. 2017

Concurrency and Computation

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SUMMARYThe Graph500 benchmark attempts to steer the design of High-Performance Computing systems to maximize the performance under memory-constricted application workloads. A realistic simulation of such benchmarks for architectural research is challenging due to size and detail limitations. By contrast, synthetic traffic workloads constitute one of the least resource-consuming methods to evaluate the performance. In this work, we provide a simulation tool for network architects that need to evaluate the suitability of their interconnect for BigData applications. Our development is a low computation-and memory-demanding synthetic traffic model that emulates the behavior of the Graph500 communications, and is publicly available in an open-source network simulator. The characterization of network traffic is inferred from a profile of several executions of the benchmark with different input parameters. We verify the validity of the equations in our model against an execution of the benchmark with a different set of parameters. Furthermore, we identify the impact of the node computation capabilities and network characteristics in the execution time of the model in a Dragonfly network.

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Section: Related Workmentioning

confidence: 99%

“…Equation 16 refers said model, where the functions f 1 (l, d r ) and f 2 (l, d r ) are determined by the formulas in Equations 8 to 15. These equations reflect a high divergence for roots with low degree, which have a much larger number of samples in the graph.…”

Section: Number Of Explored Edges Per Tree Levelmentioning

confidence: 99%

A scalable synthetic traffic model of Graph500 for computer networks analysis

Fuentes

Benito

Vallejo

et al. 2017

Concurrency and Computation

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“…Wu et al characterize 3 GPU graph frameworks and suggest to focus on constructing efficient operators. Beamer et al also measure 3 graph libraries and propose processor architecture change. Green‐Marl is a domain specific language for graph processing.…”

Section: Related Workmentioning

confidence: 99%

Efficient and high‐quality sparse graph coloring on GPUs

Chen

Fang

et al. 2016

Concurrency and Computation

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Summary Graph coloring has been broadly used to discover concurrency in parallel computing. To speed up graph coloring for large‐scale datasets, parallel algorithms have been proposed to leverage modern GPUs. Existing GPU implementations either have limited performance or yield unsatisfactory coloring quality (too many colors assigned). We present a work‐efficient parallel graph coloring implementation on GPUs with good coloring quality. Our approach uses the speculative greedy scheme, which inherently yields better quality than the method of finding maximal independent set. To achieve high performance on GPUs, we refine the algorithm to leverage efficient operators and alleviate conflicts. We also incorporate common optimization techniques to further improve performance. Our method is evaluated with both synthetic and real‐world sparse graphs on the NVIDIA GPU. Experimental results show that our proposed implementation achieves averaged 4.1 × (up to 8.9 × ) speedup over the serial implementation. It also outperforms the existing GPU implementation from the NVIDIA CUSPARSE library (2.2 × average speedup), while yielding much better coloring quality than CUSPARSE.

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“…The performance bottleneck of big data graph applications is typically the DRAM access latency due to the low computeto-memory ratios and random memory access patterns [7]. Most previous works on hardware accelerators assume that data resides at a local memory with fixed latency.…”

Section: Introductionmentioning

confidence: 99%

A Template-Based Design Methodology for Graph-Parallel Hardware Accelerators

Ayupov

Yesil

Özdal

et al. 2018

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-Graph applications have been gaining importance in the last decade due to emerging big data analytics problems such as web graphs, social networks, and biological networks. For these applications, traditional CPU and GPU architectures suffer in terms of performance and power consumption due to irregular communications, random memory accesses, and load balancing problems. It has been shown that specialized hardware accelerators can achieve much better power and energy efficiency compared to the general purpose CPUs and GPUs. In this work, we present a template-based methodology specifically targeted for hardware accelerator design of big-data graph applications. Important architectural features that are key for energy efficient execution are implemented in a common template. The proposed template-based methodology is used to design hardware accelerators for different graph applications with little effort. Compared to an application-specific high-level synthesis (HLS) methodology, we show that the proposed methodology can generate hardware accelerators with up to 18x better energy efficiency and requires less design effort.

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

Cited by 144 publications

References 38 publications

A scalable synthetic traffic model of Graph500 for computer networks analysis

A scalable synthetic traffic model of Graph500 for computer networks analysis

Efficient and high‐quality sparse graph coloring on GPUs

A Template-Based Design Methodology for Graph-Parallel Hardware Accelerators

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