PPT-Accelerating Applications with Pattern-specific Optimizations on Accelerators and Coprocessors

Author : olivia-moreira | Published Date : 2018-11-06

Linchuan Chen Advisor Dr Gagan Agrawal Motivation Platforms Accelerators are Evolving General Purpose Graphics Processing Units GPGPU Extremescale costeffective

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Accelerating Applications with Pattern-specific Optimizations on Accelerators and Coprocessors: Transcript

Linchuan Chen Advisor Dr Gagan Agrawal Motivation Platforms Accelerators are Evolving General Purpose Graphics Processing Units GPGPU Extremescale costeffective power efficient. LPAR 2008 . –. Doha, Qatar. Nikolaj . Bjørner. , . Leonardo de Moura. Microsoft Research. Bruno . Dutertre. SRI International. Satisfiability Modulo Theories (SMT). Accelerating lemma learning using joins. Nuno Lopes . and. José Monteiro. Deriving preconditions by hand is hard; WPs are often non-trivial. WPs derived by hand are often wrong!. Weaker preconditions expose more optimization opportunities. in . Dyninst. Andrew . Bernat. , Matthew Legendre. Instrumentation is Complicated. User perspective: . “Insert some new code here, here, and here.”. Dyninst’s. perspective:. Relocation. – Move code to make space for instrumentation. and High-Coverage Tests. Presented by Yoni Leibowitz. EXE & KLEE. Automatically Generating Inputs of Death. David Dill. Vijay . Ganesh. Cristian Cadar. Dawson Engler. Peter Pawlowski. KLEE. EXE. Unassisted & Automatic Generation of High-Coverage Tests for Complex System Programs. Third in a series of FP6/7 IAs on Accelerator R&D:. . C. oordinated . A. ccelerator . R. esearch in . E. urope (FP6) - 2003 to 2007 - coordinated by CEA - 22 partners. . Eu. ropean . By Namita Dave. Overview. What are compiler optimizations?. Challenges with optimizations. Current Solutions. Machine learning techniques. Structure of Adaptive compilers. Introduction. O. ptimization . Prof. O. . Nierstrasz. Lecture notes by Marcus . Denker. © Marcus . Denker. Optimization. Roadmap. Introduction. Optimizations in the Back-end. The Optimizer. SSA Optimizations. Advanced Optimizations. Third in a series of FP6/7 IAs on Accelerator R&D:. . C. oordinated . A. ccelerator . R. esearch in . E. urope (FP6) - 2003 to 2007 - coordinated by CEA - 22 partners. . Eu. ropean . ITS Research Computing. Mark Reed . Objectives. Learn why computing with accelerators is important. Understand accelerator hardware. Learn what types of problems are suitable for accelerators. Survey the programming models available. Twiss. Parameterization (left out yesterday). The transfer matrix over any period (. s. . s+C. ). must be stable over infinite passes. Must have purely imaginary eigenvalues. Any such matrix can be represented as. IFIC, Valencia Oct. 2010. . I. Vila IFCA (CSIC-UC) . Advanced European Infrastructures for Detectors at Accelerators. _Outline. News and current status. AIDA basics. Work Packages. Organization. and High-Coverage Tests. Presented by Yoni Leibowitz. EXE & KLEE. Automatically Generating Inputs of Death. David Dill. Vijay . Ganesh. Cristian Cadar. Dawson Engler. Peter Pawlowski. KLEE. EXE. Unassisted & Automatic Generation of High-Coverage Tests for Complex System Programs. 2. Accelerators. for . Medicine. Maurizio Vretenar, CERN. Academic. Training, 12 . June. 2018. Accelerator and Society. 12/6/2018. M. Vretenar, Accelerators for Medicine. 3. Research. 6%. Particle. Report from Discussion Session. Jose Alonso. 1. Charge (Per Janet). Credible case needs to be made to DUSEL in very near future. Address relevant technical (not cost, now) issues. What questions need to be answered related to feasibility of accelerator concept?.

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