PPT-Analysis of Cache Tuner Architectural Layouts for Multicore

Also Affiliated with NSF Center for HighPerformance Reconfigurable Computing This work was supported by National Science Foundation NSF grant CNS0953447 Tosiron Adegbija 1 Ann GordonRoss. 1489 Vector Research 1061 Victor 1055 Vidikron 1047 1103 Vidtech 1004 1005 Viewsonic 1361 1489 1495 Viking 1100 Vizio 1489 Wards 1000 1001 1004 1005 1031 1044 1047 1048 1053 1056 1057 1059 1060 1061 1063 1064 1103 1118 Waycon 1085 Westinghouse 136 7 656 JPEG Encoder Scaler H264MPEG2 TranscoderEncoder SX3 1Gbit FCRAM Message Passing Sharedmemory single copy of shared data in memory threads communicate by readingwriting to a shared location Messagepassing each thread has a copy of data in its own private memory that other threads cannot access threads communicate Marc De Melo. Outline. Non-Uniform Cache Architecture (NUCA). Cache Coherence. Implementation of directories in multicore architecture. 2. Non-Uniform Cache Architecture [1]. Uniform Cache Architecture. Software Architectures. 2. What Is Architectural Analysis?. Architectural analysis. is the activity of discovering important system properties using the system. ’. s architectural models.. Early, useful answers about relevant architectural aspects. – . From hardware prospective to software. Presenter: D96943001 . 電子所 陳泓輝. Why Moore’s Law is die. He is not CEO anymore!!. Walls => ILP, Frequency, Power, Memory walls. ILP – more cost less return. Multicores. Minshu. Zhao. Outline. Introduction. Review of Power management technique. Power management in . Multicore. Identify . Multicores. Characteristics. Apply power management technique. Future of . of . Safety-Critical . RT Software . to Multicore. Marco . Caccamo. University of Illinois. at Urbana-Champaign. Outline. Motivation. Memory. -centric scheduling . theory. Background: . PRedictable. Execution Model (PREM). G. Narayanaswamy. , . P. Balaji. and . W. Feng. Dept. of Comp. Science. Virginia Tech. Mathematics and Comp. Science. Argonne National Laboratory. High-end Computing Trends. High-end Computing (HEC) Systems. Hakim Weatherspoon. CS 3410, Spring 2015. Computer Science. Cornell University. P&H Chapter 2.11, 5.10,. and 6.5. Announcements. HW2 Review Sessions!. TODAY, Tue, . April 21st. , . Hollister B14@7pm. of Cyber-Physical . Systems. Akshay . Rajhans, . Shang-Wen . Cheng, . Bradley . Schmerl, . David . Garlan, Bruce H. . . Krogh, . Clarence . Agbi and . Ajinkya Bhave. Dept. of Electrical and Computer Engineering. Hierarchy with Hi-Spade. . Phillip B. Gibbons. Intel Labs Pittsburgh. September 22, 2011. Abstract. The . goal of the Hi-Spade project is to enable a hierarchy-savvy approach to algorithm design and systems for emerging parallel hierarchies. Good performance often requires effective use of the cache/memory/storage hierarchy of the target computing platform. Two recent trends---pervasive multi-cores and pervasive flash-based SSDs---provide both new challenges and new opportunities for maximizing performance. The project seeks to create abstractions, tools and techniques that (. of Cyber-Physical . Systems. Akshay . Rajhans, . Shang-Wen . Cheng, . Bradley . Schmerl, . David . Garlan, Bruce H. . . Krogh, . Clarence . Agbi and . Ajinkya Bhave. Dept. of Electrical and Computer Engineering. Cache Craftiness for Fast Multicore Key-Value Storage Yandong Mao (MIT), Eddie Kohler (Harvard), Robert Morris (MIT) Let’s build a fast key-value store KV store systems are important Google Bigtable