PPT-Cache Why it’s needed: Cost-performance optimization

Why it works The principle of locality How it works The architectural details Von Neumann Architecture Costperformance analysis is a constant theme in computer engineering which is why the proper choice of performance metric is important. Replacement Using Re-Reference . Interval . Prediction (RRIP. ). Aamer Jaleel, Kevin B. Theobald. , . Simon C. Steely Jr. , Joel . Emer. Intel . Corporation. 1. / 20. The ACM IEEE International Symposium on Computer Architecture . Hardware Accelerators. Yakun. Sophia Shao, Sam Xi,. Viji. . Srinivasan. , . Gu-Yeon. Wei, David Brooks. More accelerators.. Out-of-Core. Accelerators. Maltiel. Consulting . estimates. 2. [Die photo from . Applications. Komal Kasat. Gaurav Chitroda. Nalini Kumar. Outline . Introduction. MPEG-4. Architecture. Simulation. Results . Conclusion. INTRODUCTION. Multimedia. Combination of graphics, video, audio. S. Narravula, P. Balaji, K. Vaidyanathan, . S. Krishnamoorthy, J. Wu and D. K. Panda. The Ohio State University. Presentation Outline. Introduction/Motivation. Design and Implementation. Experimental Results. Kepler. Zehuan Wang. zehuan@nvidia.com. Fundamental Optimization. Optimization Overview. GPU . a. rchitecture. Kernel optimization. Memory optimization. Latency optimization. Instruction . optimization. Characterizing Roles of Front-end Servers in . End. -to-. End. . Performance of Dynamic Content Distribution . 46842197. Li . ZHANG. 78884704 . . Dakuo WANG. 30165502 . . Xuejie. SUN. 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. Stender. Chapter 13 of Constraint Processing by . Rina. . Dechter. 3/25/2013. 1. Constraint Optimization. Motivation. 3/25/2013. 2. Constraint Optimization. Real-life problems often have both . hard. Kadin Tseng. Boston University. Scientific Computing and Visualization. Outline. Introduction. Timing. Example Code. Profiling. Cache. Tuning. Parallel Performance. Code Tuning and Optimization. 2. Introduction. With a superscalar, we might need to accommodate more than 1 per cycle. Typical server and . m. obile device. memory hierarchy. c. onfiguration with. b. asic sizes and. access times. PCs and laptops will. March 28, 2017. Agenda. Review from last lecture. Cache access. Associativity. Replacement. Cache Performance. Cache Abstraction and Metrics. Cache hit rate = (# hits) / (# hits # misses) = (# hits) / (# accesses). Classification of algorithms. The DIRECT algorithm. Divided rectangles. Exploration and Exploitation as bi-objective optimization. Application to High Speed Civil Transport. Global optimization issues. Michael Kantor. CEO and Founder . Promotion Optimization Institute (POI). First Name. Last Name. Company. Title. Denny. Belcastro. Kimberly-Clark. VP Industry Affairs. Pam. Brown. Del Monte. Director, IT Governance & PMO. SNIA Forward Looking Information Disclosure Statement. 2. This SNIA presentation as part of the industry EPA ENERGYSTAR Data Center Storage Stakeholders Meeting November 18 2015  may include timetables, roadmaps, new technologies entering the...