PPT-A Scalable Heterogeneous Parallelization Framework for

Author : mercynaybor | Published Date : 2020-08-27

Iterative Local Searches Martin Burtscher 1 and Hassan Rabeti 2 1 Department of Computer Science Texas State UniversitySan Marcos 2 Department of Mathematics Texas

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A Scalable Heterogeneous Parallelization Framework for: Transcript

Iterative Local Searches Martin Burtscher 1 and Hassan Rabeti 2 1 Department of Computer Science Texas State UniversitySan Marcos 2 Department of Mathematics Texas State UniversitySan Marcos. Ann Chervenak 1 Ewa Deelman 1 Ian Foster 2,3 Leanne Guy 4 Wolfgang Hoschek 4 Adriana Iamnitchi 2 Carl Kesselman 1 Peter Kunszt Matei Ripeanu 2 Bob Schwartzkopf 1 Hein Kaushik. . Rajan. Abhishek. . Udupa. William Thies. Rigorous Software Engineering. Microsoft Research, India. Parallelization Reconsidered. Are there dependences between loop iterations?. No. Yes. DOALL Parallelism. Yizhou. Sun, Rick Barber, Manish Gupta, . Charu. . C. . Aggarwal. , . Jiawei. Han. 1. Content. Background and motivation. Problem definition. PathPredict. : meta path-based . relationship prediction . 12/01/2009. . Air France, Sophia . Antipolis. . . Solutions. and Services for . Accelerating. . your. Applications. . ProActive Parallel Suite . . Speed-up . applications by distributing them over a set of resources . Anthony Waterman. Topics to Discuss. Are online games . c. onceptually. . p. arallel?. What portions of a game benefit from parallelization?. Graphics Processing Units (GPUs) . General-Purpose . C. omputing . Kaushik. . Rajan. Abhishek. . Udupa. William Thies. Rigorous Software Engineering. Microsoft Research, India. Parallelization Reconsidered. Are there dependences between loop iterations?. No. Yes. DOALL Parallelism. Yizhou. Sun, Rick Barber, Manish Gupta, . Charu. . C. . Aggarwal. , . Jiawei. Han. 1. Content. Background and motivation. Problem definition. PathPredict. : meta path-based . relationship prediction . on Single-chip Shared-memory Multicores. Masab. Ahmad, . Kartik. . Lakhsminrarsimhan. , Omer Khan. University of Connecticut. Agenda. Motivation. Characterization Methodology. Characterization Results. Presented by: . Dardan. . Xhymshiti. Spring 2016. Paper . Information. Authors. :. Publication. :. ICDE 2015. Type:. Research Paper. 2. Sean Chester*. Darius . Sidlauskas. `. Ira Assent*. Kenneth S. . Konstantinos Theodorakos. January 2015. Modern Processor Design. “Free lunch is over”. Lower Power consumption is favored on multi-core/processor architectures. CBD Parallelization . intention. Why Parallelize CBD models?. ECE 751, Fall 2015. Peng . Liu. 1. Overview. What? JavaScript . Engine optimization. How? Light-weight . software speculation mechanism. 2. [1] Heine. , David, et al. Software and hardware for exploiting speculative parallelism with a multiprocessor. Computer Systems Laboratory, Stanford University, 1997. Qi Hu, Nail A. Gumerov, Ramani Duraiswami. Institute for Advanced Computer Studies . Department of Computer Science. University of Maryland, College Park, MD. Previous work. FMM on distributed systems. Objective: develop technologies to improve computer performance. . . 1. Processor. Generation. Max. Clock. Speed (GHz). Max. Numberof Cores. Max. RAM. Bandwidth (GB/s). Max. Peak Floating Point (Gflop/s). 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.

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