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 Carl Yang

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Bibliometrics: publication history
Average citations per article2.00
Citation Count8
Publication count4
Publication years2015-2017
Available for download2
Average downloads per article180.00
Downloads (cumulative)360
Downloads (12 Months)232
Downloads (6 Weeks)36
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1 published by ACM
August 2017 ACM Transactions on Parallel Computing (TOPC) - Special Issue: Invited papers from PPoPP 2016, Part 1: Volume 4 Issue 1, October 2017
Publisher: ACM
Citation Count: 1
Downloads (6 Weeks): 26,   Downloads (12 Months): 165,   Downloads (Overall): 170

Full text available: PDFPDF
For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. “Gunrock,” our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on ...
Keywords: runtime framework, GPU, Graph processing

2 published by ACM
May 2016 HPGP '16: Proceedings of the ACM Workshop on High Performance Graph Processing
Publisher: ACM
Citation Count: 1
Downloads (6 Weeks): 10,   Downloads (12 Months): 67,   Downloads (Overall): 190

Full text available: PDFPDF
We implement exact triangle counting in graphs on the GPU using three different methodologies: subgraph matching to a triangle pattern; programmable graph analytics, with a set-intersection approach; and a matrix formulation based on sparse matrix-matrix multiplies. All three deliver best-of-class performance over CPU implementations and over comparable GPU implementations, with ...
Keywords: graph processing, triangle counting, gpu computing, parallel

October 2015 IISWC '15: Proceedings of the 2015 IEEE International Symposium on Workload Characterization
Publisher: IEEE Computer Society
Citation Count: 2

We identify several factors that are critical to high-performance GPU graph analytics: efficient building block operators, synchronization and data movement, workload distribution and load balancing, and memory access patterns. We analyze the impact of these critical factors through three GPU graph analytic frameworks, Gun rock, Map Graph, and VertexAPI2. We ...

May 2015 IPDPSW '15: Proceedings of the 2015 IEEE International Parallel and Distributed Processing Symposium Workshop
Publisher: IEEE Computer Society
Citation Count: 2

We implement a promising algorithm for sparse-matrix sparse-vector multiplication (SpMSpV) on the GPU. An efficient k-way merge lies at the heart of finding a fast parallel SpMSpV algorithm. We examine the scalability of three approaches -- no sorting, merge sorting, and radix sorting -- in solving this problem. For breadth-first ...
Keywords: parallel, GPU, graph algorithm, sparse matrix multiplication

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