PPT-Code Optimization

Data Flow Analysis Data Flow Analysis DFA General framework Can be used for various optimization goals Some terms Basic block A sequence of statements without branch Point Before or after each statement. Real-time Performance. Presenter: Raj Johnson. Webinar Agenda. Porting Application to Linux . using. . OS Changer. Improving Application’s Performance using. . Linux OK . (. Linux Optimization Kit. Summary, Anti-summary, and . Final . T. houghts. Summary (1) Architecture. Modern architecture designs are driven by energy constraints. Shortening latencies is too costly, so we use parallelism in hardware to increase potential throughput. spire. Lua. Advanced Scripting techniques. Adrien BERTRAND . (« Adriweb »). Table of Contents. Code Optimization. Lua Performance Benchmarks. Tips and Tricks. Nspire-Lua Specific Things. Advanced techniques in practice. Lua Programming. n. spire. TI. . -. Code optimization . . &. . Alternative Lua Editors. Adrien BERTRAND (« Adriweb »). Table of Contents. Code Optimization. Lua Performance Benchmarks. Tips and Tricks. D. A. Gates. 1. , A. H. Boozer. 2. , T. Brown. 1. , J. Breslau. 1. , D. Curreli. 3. , M. Landreman. 4. , S. A. Lazerson. 1. , . J. Lore. 5. , H. . Mynick. 1. , G.H. Neilson. 1. , N. Pomphrey. 1. , P. . Dawei Fan. Contents. Introduction. 1. Methodology. 2. RTL Design and Optimization. 3. Physical Layout Design. 4. Conclusion. 5. Introduction. What is convolution?. Convolution . is defined as the . Or, How much wood could a woodchuck chuck if a woodchuck could chuck . n. pieces of wood in parallel?. Wojtek Rajski, Nels Oscar, David Burri, Alex Diede. Introduction. We have seen how to improve performance through exploitation of:. Real-time Performance. Presenter: Raj Johnson. Webinar Agenda. Porting Application to Linux . using. . OS Changer. Improving Application’s Performance using. . Linux OK . (. Linux Optimization Kit. Prof. O. . Nierstrasz. Lecture notes by Marcus . Denker. © Marcus . Denker. Optimization. Roadmap. Introduction. Optimizations in the Back-end. The Optimizer. SSA Optimizations. Advanced Optimizations. Programming Languages. . Performance Patterns, Macros. Zach . Tatlock. Spring 2014. When I was a novice programmer, I spent many hours trying to "speed up" . my code. . One day, after many agonizing attempts at this feat, my . Computer Science & Engineering Department. The University of Connecticut. 371 Fairfield Way, Unit 2155. Storrs, CT 06269-3155. steve@engr.uconn.edu. http://www.engr.uconn.edu/~steve. (860) 486 - 4818. Kadin Tseng. Boston University. Scientific Computing and Visualization. Outline. Introduction. Timing. Example Code. Profiling. Cache. Tuning. Parallel Performance. Code Tuning and Optimization. 2. Introduction. Computer Science & Engineering Department. The University of Connecticut. 371 Fairfield Way, Unit 2155. Storrs, CT 06269-3155. steve@engr.uconn.edu. http://www.engr.uconn.edu/~steve. (860) 486 - 4818. Prof. Gennady . Pekhimenko. University of Toronto. Winter 2018. The content of this lecture is adapted from the lectures of . Todd Mowry and Phillip Gibbons. CSC D70: . Compiler Optimization. Introduction, Logistics.