PPT-Optimization and Parallelization of CBD models

Konstantinos Theodorakos January 2015 Modern Processor Design Free lunch is over Lower Power consumption is favored on multicoreprocessor architectures CBD Parallelization intention Why Parallelize CBD models. Kaushik. . Rajan. Abhishek. . Udupa. William Thies. Rigorous Software Engineering. Microsoft Research, India. Parallelization Reconsidered. Are there dependences between loop iterations?. No. Yes. DOALL Parallelism. Prof . Erik Dahlquist. Malardalen . University. e. rik.dahlquist@mdh.se. Objectives. The . aim. of . this. . application. is . to. . build. a . foundation. of . mathematical. . tools. for . application. Kaushik. . Rajan. Abhishek. . Udupa. William Thies. Rigorous Software Engineering. Microsoft Research, India. Parallelization Reconsidered. Are there dependences between loop iterations?. No. Yes. DOALL Parallelism. Katya Scheinberg. Lehigh University. (mainly based on work with . A. . Bandeira. and L.N. . Vicente and also with A.R. Conn, . Ph.Toint. . and C. . Cartis. ). 08/20/2012. ISMP 2012. 08/20/2012. ISMP 2012. multilinear. gradient elution in HPLC with Microsoft Excel Macros. Aristotle University of Thessaloniki. A. . Department of Chemistry, Aristotle University of . Thessaloniki. B. Department of Chemical Engineering, Aristotle University of Thessaloniki. ECE 751, Fall 2015. Peng . Liu. 1. Overview. What? JavaScript . Engine optimization. How? Light-weight . software speculation mechanism. 2. [1] Heine. , David, et al. Software and hardware for exploiting speculative parallelism with a multiprocessor. Computer Systems Laboratory, Stanford University, 1997. Introduction. In many complex optimization problems, the objective and/or the constraints are . nonlinear functions . of the decision variables. Such optimization problems are called . nonlinear programming . Introduction. In many complex optimization problems, the objective and/or the constraints are . nonlinear functions . of the decision variables. Such optimization problems are called . nonlinear programming . A wide variety of problems can be formulated as linear. . programming . models, but there are some that cannot.. Some models require . integer . variables, . or they are . nonlinear . in the decision variables. Novelty 1: Ice thickness is allowed to vary during the optimization (but constrained by observational uncertainties) to provide another degree of freedom. Probabilistic Sea-Level Projections from Ice Sheet and Earth System Models 3:  Iterative Local Searches. Martin . Burtscher. 1. and Hassan Rabeti. 2. 1. Department of Computer Science, Texas State University-San Marcos. 2. Department of Mathematics, Texas State University-San Marcos. Iterative Local Searches. Martin . Burtscher. 1. and Hassan Rabeti. 2. 1. Department of Computer Science, Texas State University-San Marcos. 2. Department of Mathematics, Texas State University-San Marcos. Parameter estimation, gait synthesis, and experiment design. Sam Burden, Shankar . Sastry. , and Robert Full. Optimization provides unified framework. 2. ?. ?. ?. ?. ?. Blickhan. & Full 1993. Srinivasan. Probabilistic Sea-Level Projections from Ice Sheet and Earth System Models 3: . Performance, Optimization and Uncertainty Quantification. BISICLES - Dan. recomputations . Project Members:. Stephen Price (PI; LANL),  Esmond Ng (PI; LBNL), .