PPT-Computing beyond a Million Processors

Author : phoebe-click | Published Date : 2017-11-03

bioinspired massivelyparallel architectures Steve Furber The University of Manchester stevefurbermanchesteracuk SBF is supported by a Royal Society Wolfson Research

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Computing beyond a Million Processors: Transcript

bioinspired massivelyparallel architectures Steve Furber The University of Manchester stevefurbermanchesteracuk SBF is supported by a Royal Society Wolfson Research Merit Award Andrew Brown. 14 2.0 l 20 1 Tetenal AG & CO.KG 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-. 6th Edition. . Chapter 12: Floating-Point Processing and Instruction Encoding. (c) Pearson Education, 2010. All rights reserved. You may modify and copy this slide show for your personal use, or for use in the classroom, as long as this copyright statement, the author's name, and the title are not changed.. Pavan Balaji,. 1. Darius Buntinas,. 1. David Goodell,. 1. . William Gropp,. 2. Sameer Kumar,. 3. Ewing Lusk,. 1. . Rajeev Thakur. ,. 1. Jesper Larsson Tr. ä. ff . 4. 1. Argonne National Laboratory. Reconfigurable Computing. http://www.ece.arizona.edu/~ece506. Lecture 1. Course Introduction. Ali Akoglu. Background needed for this course. You should be familiar with:. Digital design. Architecture. Improving Computer Performance. What performance translates into:. Time taken to do computation. Improving performance . → reducing time taken. What key benefits improving performance can bring:. Can solve “now-computationally-attainable” problems in . Contents . Vector processor. Vector instructions. Vector pipelines. Scalar pipeline execution. Vector pipeline execution. Symbolic processors. Attributes. Characteristics. Vector Processors. A vector processor is specially designed to perform vector computations.. 14 2.0 l 20 1 Tetenal AG & CO.KG Evangelia A. Sitaridi, Kenneth A. Ross. Columbia University. DaMoN Workshop. 21st May 2012. . Introduction (1/2). Earlier GPU implementations of data processing operators have resulted in significant speedups. Ahmed . Sameh. and . Ananth. . Grama. NNSA/PRISM Center,. Purdue University. Path to . Exascale. Hardware Evolution. Key Challenges for Hardware. System Software. Runtime Systems. Programming Interface/ Compilation Techniques. CSCE 190: Computing in the Modern World. Jason D. Bakos. Heterogeneous and Reconfigurable Computing Group. Apollo 11 vs iPhone 10. Apollo 11. Cost: ~$200 billion. (whole program, adjusted). Guidance Computer (1966):. PG7: . Trigger, Online, Offline and Computing. . preparatory group. Wesley . H. Smith. U. Wisconsin - Madison. . Graeme Stewart. U. Glasgow. June 13, 2014. Membership. ALICE: . Latchezar. . Betev. Tennessee State University. 2017. 年. 6. 月. at. 法政大学. 1. Lectures on Parallel and Distributed Computing . 2. Lecture . 1: Introduction to parallel . computing . Lecture 2: Parallel . computational models. The global high-speed camera market is estimated to have reached USD 450.2 Million in 2020 and is further projected to reach USD 876.6 Million by 2027, growing at a CAGR of 10.2% during the forecast period

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