PPT-Scalable SIMD-Efficient Graph Processing on GPUs

Farzad Khorasani Rajiv Gupta Laxmi N Bhuyan University of California Riverside Scalable SIMDEfficient Graph Processing on GPUs Graph Processing Building blocks of data analytics. Recap: data-intensive cloud computing. Just database management on the cloud. But scaling it to thousands of nodes. Handling partial failures gracefully. Sacrificing strong ACID. Graph processing frameworks (. SIMD architectures. A data parallel architecture. Applying the same instruction to many data. Save control logic. A related architecture is the vector architecture. SIMD and vector architectures offer high performance for . Marc S. Orr. †§. , Bradford M. Beckmann. §. , Steven K. Reinhardt. §. , David A. Wood. †§. ISCA, June 16, 2014. †. §. Executive Summary. SIMT languages (e.g. CUDA & . OpenCL. ) restrict GPU programmers to regular parallelism. Or, How much wood could a woodchuck chuck if a woodchuck could chuck . n. pieces of wood in parallel?. Wojtek Rajski, Nels Oscar, David Burri, Alex Diede. Introduction. We have seen how to improve performance through exploitation of:. Farzad Khorasani. , . Rajiv Gupta. , . Laxmi. N. . Bhuyan. University of California Riverside. Scalable SIMD-Efficient Graph Processing on GPUs. Graph Processing. Building blocks of data analytics.. on OpenCL-based FPGAs. Zeke Wang. , Johns Paul, Hui Yan Cheah . (. NTU,. . Singapore), . Bingsheng He (. NUS,. Singapore), . Wei Zhang (HKUST, Hong Kong). 1. Outline. Background and Problem. Challenges. Shashwat Shriparv. dwivedishashwat@gmail.com. InfinitySoft. 2. 10/30/2010. Presentation Overview. Definition. Comparison with CPU. Architecture. GPU-CPU Interaction. GPU Memory. 10/30/2010. 3. Why GPU?. NSF 1443054: CIF21 DIBBs: Middleware and High Performance Analytics Libraries for Scalable Data Science. Big Data . Use Cases February 2017. 1. Application Nexus of HPC, Big Data, Simulation Convergence. Abbas Rahimi. , Amirali Ghofrani, Kwang-Ting Cheng, Luca Benini, Rajesh K. . Gupta. UC San Diego. , UC Santa Barbara, ETH Zurich. NSF Variability Expedition. ERC . MultiTherman. Motivation. Energy Efficiency in GPUs. . with Distributed Immutable View. Rong Chen. +. , . Xin. Ding. +. , . Peng. Wang. +. , Haibo Chen. +. , . Binyu . Zang. +. and Haibing Guan. *. Institute of Parallel and Distributed Systems. +. Iterative Local Searches. Martin . Burtscher. 1. and Hassan Rabeti. 2. 1. Department of Computer Science, Texas State University-San Marcos. 2. Department of Mathematics, Texas State University-San Marcos. Lingxiao Ma. . †. , Zhi Yang. . †. , Youshan Miao. ‡. , Jilong Xue. ‡. , Ming Wu. ‡. , Lidong Zhou. ‡. , . Yafei. Dai. . †. †. . Peking University. ‡ . Microsoft Research. USENIX ATC ’19, Renton, WA, USA. Stijn Eyerman, Wim Heirman, Ibrahim Hur, Joshua B. Fryman. 2. DARPA HIVE project. “To build a graph analytics processor that can process streaming graphs 1000X faster and at much lower power than current processing technology”. MRNet. and GPUs. Evan . Samanas. and Ben . Welton. Density-based clustering. Discovers the number of clusters. Finds oddly-shaped clusters. 2. Mr. Scan: Efficient Clustering with . MRNet. and GPUs.