PPT-Cache Optimization Summary

Technique MR MP HT Complexity Larger Block Size 0 Higher Associativity 1 Victim Caches 2 PseudoAssociative Caches 2 HW Prefetching of InstrData 2 Compiler Controlled Prefetching . Huang Dept of Electrical Computer Engineering Dept of Computer Science University of Rochester Rochester NY 14627 USA pariharece cdingcs michaelhuang rochesteredu Abstract A hot concept in program optimization is hotness For example program optimiz Lam Edward E Rothberg and Michael E Wolf Computer Systems Laboratory Stanford University CA 94305 Abstract Blocking is wellknown optimization technique for improving the effectiveness of memory hierarchies Instead of operating on entire rows or colu Applications. Komal Kasat. Gaurav Chitroda. Nalini Kumar. Outline . Introduction. MPEG-4. Architecture. Simulation. Results . Conclusion. INTRODUCTION. Multimedia. Combination of graphics, video, audio. ENCODER. EEL 6935 Embedded Systems. Long Presentation 2. Group Member: Qin Chen, Xiang Mao. ECE@UFL. 4/2/2010. 1. Outline. Design goals and challenges. Video encoding basics. Memory/cache optimization. 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. . . Advanced Topics. . Lihu Rappoport and Adi Yoaz. Intel. ®. . Core. TM. . μ. Arch. Haswell builds upon innovations in the . 2. nd. and 3. rd. Generation Intel. ®. Core™ i3/i5/i7 Processors (Sandy Bridge and Ivy Bridge). Loop Transformations. Chapter 11.10-11.11. Dror. E. . Maydan. CS243: Loop Optimization and Array Analysis. 1. Loop Optimization. . Domain. Loops: Change the order in which we iterate through loops. Kepler. Zehuan Wang. zehuan@nvidia.com. Fundamental Optimization. Optimization Overview. GPU . a. rchitecture. Kernel optimization. Memory optimization. Latency optimization. Instruction . optimization. cache Directive:. Opportunities and Optimizations. Ahmad . Lashgar. & . Amirali. . Baniasadi. ECE Department. University of Victoria. November . 14. , 2016. 1. Directive-based Accelerator Programming Models. Technique MR MP HT Complexity. Larger Block Size + – . 0. Higher Associativity + – 1. Victim Caches + 2. Pseudo-Associative Caches + 2. HW Prefetching of Instr/Data + 2. Compiler Controlled Prefetching + . Chris . Wilcox, Michelle Mills . Strout. , James M. . Bieman. Computer Science Department. Colorado State University. Source Control Analysis and Manipulation (SCAM). Riva del Garda, Italy – September 23, 2012. Kadin Tseng. Boston University. Scientific Computing and Visualization. Outline. Introduction. Timing. Example Code. Profiling. Cache. Tuning. Parallel Performance. Code Tuning and Optimization. 2. Introduction. Why it works: The principle of locality. How it . works. : The architectural details. Von Neumann Architecture. Cost/performance analysis is a constant theme in computer engineering ‒ which is why the proper choice of performance metric is important. Direct-mapped caches. Set-associative caches. Impact of caches on performance. CS 105. Tour of the Black Holes of Computing. Cache Memories. C. ache memories . are small, fast SRAM-based memories managed automatically in hardware.