PPT-Scalable SIMD-Efficient Graph Processing on GPUs

Author : jane-oiler | Published Date : 2018-02-20

Farzad Khorasani Rajiv Gupta Laxmi N Bhuyan University of California Riverside Scalable SIMDEfficient Graph Processing on GPUs Graph Processing Building blocks

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Scalable SIMD-Efficient Graph Processing on GPUs: Transcript

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 (. David . Kaser. Lecture Series. Lorcan Dempsey / . @. LorcanD. Indiana University, . 7 October 2012. How terrific to see you are the featured lecturer this year. Just thought I'd mention that David . Tailoring . SIMD Execution using . Heterogeneous Hardware . and Dynamic Configurability. Yongjun Park. 1. , Jason Jong Kyu Park. 1. , Hyunchul Park. 2. ,. and Scott Mahlke. 1. December. 3, 2012. 1. Processing . 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. Tailoring . SIMD Execution using . Heterogeneous Hardware . and Dynamic Configurability. Yongjun Park. 1. , Jason Jong Kyu Park. 1. , Hyunchul Park. 2. ,. and Scott Mahlke. 1. December. 3, 2012. 1. 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.. GPU. Radeon. . R700. (HD . 4xxx. ) series. Elizabeth . Soechting. David Chang. Jessica Vasconcellos. 1. CS 433 . Advanced Computer Architecture. May 7. , 2008. General. 2. General. Interconnect. SIMD. Data-Level Parallel Architectures. Course 5MD00. Henk. . Corporaal. January 2015. h.corporaal@tue.nl. This lecture. Data-level parallel architectures. Vector machine. SIMD. (Single Instruction Multiple Data) processors. christiaan. gribble. karthik. . ramani. ieee. /. eurographics. symposium on interactive ray tracing. august 2008. early implementation. andrew. . kensler. (. utah. ). ingo. . wald. (. intel. ) . 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. Bin . Ren. Advisor: Dr. . Gagan. . Agrawal. 4/6/2014. 1. Streaming SIMD Extension. Motivation. 4/6/2014. 2. Various emerging parallel architectures. Intel . . SSE. 1999. Intel . . SSE3. 2004. Chapter . 4. Data-Level Parallelism in Vector, SIMD, and GPU Architectures. Computer Architecture. A Quantitative Approach, Fifth Edition. Copyright © 2012, Elsevier Inc. All rights reserved.. Flynn’s Taxonomy. 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.

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