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. 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-. Aapo. Kyrola. Danny. Bickson. A Framework for Machine Learning and Data Mining in the Cloud . Joseph. Gonzalez. Carlos. Guestrin. Joe. Hellerstein. Big Data is Everywhere. 72 Hours . a Minute. YouTube. 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. . Parallel Speedup Estimates for Serial Programs. Donghwan. . Jeon. , . Saturnino. Garcia, Chris. Louie, . and Michael Bedford Taylor. Computer Science and Engineering. University of California, San Diego. : . 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. Feb 2, 2015. Multicore (and Shared Memory) Programming with . Cilk. Plus. Multicore and s. hared . m. emory. . Cilk. . Plus and the divide & conquer . p. aradigm. Data races. Analyzing performance in . Bickson. A Framework for Machine Learning and Data Mining in the Cloud . Joseph. Gonzalez. Carlos. Guestrin. Joe. Hellerstein. Big Data is Everywhere. 72 Hours . a Minute. YouTube. 28 . Million . Wikipedia Pages. 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 . . 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. Parallel Processing, Multicores. Prof. Gennady . Pekhimenko. University of Toronto. Fall 2017. The content of this lecture is adapted from the lectures of . Onur. . Mutlu. @ CMU. Summary . Parallelism. 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 . Performance of Computer System. Various factors. Depends on. Scalability: . Computer Performance . Number of processors used. Measuring performance of parallel computer architecture for large-scale applications require very good understanding of...

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