PDF-Superlinear Speedup in Parallel Computation Jing Shan jshan@ccs.neu.e

Author : danika-pritchard | Published Date : 2015-10-29

1 Introduction to The Problem Because of its good speedup parallel computing becomes more and more important in scientific computations especially in those involving

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Superlinear Speedup in Parallel Computation Jing Shan jshan@ccs.neu.e: Transcript


1 Introduction to The Problem Because of its good speedup parallel computing becomes more and more important in scientific computations especially in those involving largescaled data When ta. Unlike sequential algorithms parallel algorithms cannot be analyzed very well in isolation One of our primary measures of goodness of a parallel system will be its scalability Scalability is the ability of a parallel system to take advantage of incr Performance Theory - 1. Parallel Computing. CIS . 410/. 510. Department of Computer and Information Science. Outline. Performance scalability. Analytical performance measures. Amdahl. ’. s. law and Gustafson-. Goals for Rest of Course. Learn how to program massively parallel processors and achieve. high performance. functionality and maintainability. scalability across future generations. Acquire technical knowledge required to achieve the above goals. : . A . Toolchain. To Help Parallel Programming . Minjang Kim, Hyesoon Kim, . HPArch. Lab, and Chi-Keung . Luk. Intel. This work will be also supported by Samsung. Motivation (1/2). Parallel programming is hard. Lecture 10: DNS. (What’s in a Name?). Based on Slides by D. . Choffnes. (NEU). Revised by P. Gill Spring 2015. Some content on DNS censorship from N. Weaver.. Layer 8 (The Carbon-based nodes). 2. If you want to…. Lecture 10: DNS. (What’s in a Name?). Based on Slides by D. . Choffnes. (NEU). Revised by P. Gill Fall 2014. Some content on DNS censorship from N. Weaver.. Administravia. 2. Midterm: 1 week from today. Dr Susan Cartwright. Dept of Physics and Astronomy. University of Sheffield. Parallel Universes. Are you unique?. Could there be another “you” differing only in what you had for breakfast this morning?. Dr. Yingwu Zhu. Chapter 27. Motivation. We have discussed . serial algorithms. that are suitable for running on a . uniprocessor. computer. We will now extend our model to . parallel algorithms. that can run on a . TAMARA LAYNE MS,OTR/L . INTEGRATED SERVICES COORDINATOR. MILWAUKEE COUNTY’S COMMUNITY ACCESS TO . RECOVERY SERVICES (CARS) BRANCH . WELCOME TO RECOVERY, INC.. RECOVERY, INC. . recently hired all of their staff for the agency’s new CCS Program.. . Kartik . Nayak. With Xiao . Shaun . Wang, . Stratis. Ioannidis, Udi . Weinsberg. , Nina Taft, Elaine Shi. 1. 2. Users. Data. Data. Privacy concern!. Data Mining Engine. Data Model. Data Mining on User Data. . Kartik . Nayak. With Xiao . Shaun . Wang, . Stratis. Ioannidis, Udi . Weinsberg. , Nina Taft, Elaine Shi. 1. 2. Users. Data. Data. Privacy concern!. Data Mining Engine. Data Model. Data Mining on User Data. CCS . PRU. Compiler Tools introduction. 1. PRU. -ICSS Compiler History. Development cycle before . PRU. Compiler support for CCS v6.x. Notepad to edit . PRU. source code (with keyword highlight extension). Hongyi. & Shen . Tianxiao. & Pang . Lianyu. . Group 2. The page and search of papers. . and. . conferences: all. The recommendation of paper: Li . Ziyi. , Jing . Osman . Sarood. How faster can we run?. Suppose we have this serial problem with 12 tasks. How fast can we run given 3 processors?. Running in parallel. Execution time reduces from 12 . secs. to 4 .

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