PPT-Parallel Application

Memory Scheduling Eiman Ebrahimi Rustam Miftakhutdinov Chris Fallin Chang Joo Lee Jose Joao Onur Mutlu Yale N Patt HPS Research Group . 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 Dr. Guy Tel-. Zur. Lecture 10. Agenda. Administration. Final presentations. Demos. Theory. Next week plan. Home assignment #4 (last). Final Projects. Next Sunday: Groups 1-1. 6. will present. Next Monday: Groups 1. mei. W. . Hwu. , 2007-2012 . University . of Illinois, Urbana-Champaign. 1. CS/EE 217. GPU Architecture and Parallel . Programming. Project . Kickoff. Two flavors. Application. Implement/optimize an realistic application on GPGPUs. Implications . for . Software Environments.  . eScience. in the Cloud . 2014. Redmond WA. April 30 2014. Geoffrey . Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. Ketan Maheshwari. , Alex Rodriguez, David Kelly, Ravi . Madduri. , Justin Wozniak, Michael Wilde, Ian Foster. Argonne National Laboratory & University of Chicago. Overview. Introduce the Galaxy and Swift systems. Programming & Cluster Computing. Applications and. Types of Parallelism. Joshua Alexander, U Oklahoma. Ivan . Babic. , Earlham College. Michial. Green, Contra Costa College. Mobeen. . Ludin. , Earlham College. with . OpenMP. © Dan Negrut, . 2012. UW-Madison. Dan Negrut. Simulation-Based Engineering . Lab. Wisconsin Applied Computing Center. Department of Mechanical . Engineering. Department of . Electrical and Computer Engineering. 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?. Parallel Program . =. Operator Schedule Parallel data structure. SAMOS XV . Keynote. Parallel computing is changing. Platforms. Dedicated clusters . versus. cloud, mobile. Data. Structured (vector, matrix) . Justin Y. Shi | . shi@temple.edu. Temple University. reproducibility@XSEDE. | An . XSEDE14 . Workshop | July . 14, . 2014 | Atlanta. , GA. Why . Decoupling. can . I. mprove . P. rogram Reproducibility?. timeless. – as opposed to fashionable (e.g. neurolinguistics, psycholinguistics, semiotics, social psychology, theoretical linguistics, etc.). Use a generic definition of CS –an . interdisciplinary. Implications . for . Software Environments.  . eScience. in the Cloud . 2014. Redmond WA. April 30 2014. Geoffrey . Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. K3130303129/RAJAmemory and a range of various architectures which can result in decreased productivity for library and application developers who need to write specialized so31ware for each system 30e eConcertation. Meeting. Lyon, September . 22-23. , 2011. Steve . Conway . IDC Research Vice President, HPC . sconway@idc.com. . This study, "Financing a Software Infrastructure for Highly . Parallelised.