PPT-Near Optimal Work-Stealing Tree for Highly Irregular Data-Parallel Workloads

Aleksandar Prokopec Martin Odersky 1 Near Optimal WorkStealing Tree for Highly Irregular DataParallel Workloads Aleksandar Prokopec Martin Odersky Irregular DataParallel 2 Uniform workload. of Computer Science University of Illinois UrbanaChampaign jli642602illinoisedu Sriram Krishnamoorthy Comp Sci Math Division Paci64257c Northwest National Lab srirampnnlgov Laxmikant V Kale Dept of Computer Science University of Illinois UrbanaCham 6/16/2010. Work Stealing Scheduler. 1. Announcements. Text books. Assignment 1. Assignment 0 results. Upcoming Guest lectures. 6/16/2010. Work Stealing Scheduler. 2. Recommended Textbooks. 6/16/2010. Jonathan Lifflander, UIUC. Sriram Krishnamoorthy, PNNL*. Laxmikant Kale, UIUC. HPDC 2012. Dynamic load balancing on . 100,000 processor cores . and beyond. Iterative Applications. Applications repeatedly executing the same computation. Nishant Totla. , . Aditya. . Devarakonda. , . Sanjit. . Seshia. SAT Solving. Given a propositional logic formula in conjunctive normal form (CNF), does there exist a satisfying assignment?. . B. y Work-Stealing. Robert D. . Blumofe. The University of Texas, Austin. Charles E. . Leiserson. ,. MIT Laboratory for Computer Science. Motivation. Strict binding of multi-threaded computations on parallel computers.. Trent Steele. Southeast Region. Sr. Systems Engineer. (336) 482-5725. trent.steele@tintri.com. www.thevbox.net. . @. thevbox. . The Business. IT…What are we here for? . What drives your business? . Lecture 3. MADALGO Summer School 2012. Algorithms for Modern Parallel and Distributed Models . Phillip B. Gibbons. Intel Labs Pittsburgh. August 22, 2012. Multi-core Computing Lectures: . Progress-to-date on Key Open Questions. 6/16/2010. Work Stealing Scheduler. 1. Announcements. Text books. Assignment 1. Assignment 0 results. Upcoming Guest lectures. 6/16/2010. Work Stealing Scheduler. 2. Recommended Textbooks. 6/16/2010. Evaluation. . Sequential: runtime (execution time). . Ts. =T (. InputSize. ). . Parallel: runtime (. s. tart-->last PE ends). . Tp. =T (. InputSize,p,architecture. ). . Note: Cannot be Evaluated in Isolation from the Parallel architecture. 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. Highly-parallel Accelerators. Marc . S. Orr. PhD Defense, . December . 5. , . 2016. Advisor: David A. Wood. Committee. : Brad Beckmann, Mark Hill, Nam . Sung Kim,. Karu. . Sankaralingam. , and Mike Swift. Mahesh Ravishankar. 1. , John Eisenlohr. 1. , . Louis-Noël Pouchet. 1,3. , J. Ramanujam. 2. , . Atanas. Rountev. 1. , P. Sadayappan. 1. 1. Dept. of Computer Science & Engineering. The Ohio State University. Michael Champigny. Research Scientist. Advanced Computing Solutions. Mercury Computer Systems. 2010 HPEC Workshop. September 15, 2010. Motivation. Single-chip parallelism and convergence. Variability challenges. Ashok Padmaraju. Course. : Topics on Software Engineering. Instructor: . Dr. Sergiu Dascalu. Introduction to Big Data workflows. “Big Data” is a broad term for datasets that are so large or complex..