PPT-Auto-tuning Stencil Codes for Cache-Based Multicore Platforms

Kaushik Datta Dissertation Talk December 4 2009 Motivation Multicore revolution has produced wide variety of architectures Compilers alone fail to fully exploit multicore resources Handtuning has become infeasible. 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. Ray Chen, UMD. Jeff Hollingsworth, UMD. Michael P. Ferguson, LTS. Harmony. Overview. Harmony system based. on feedback loop. 2. Harmony Server. Application. Parameter. Values. Measured. Performance. – . 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 . Parallel Computing. CIS . 410/. 510. Department of Computer and Information Science. Outline. Partitioning. What is the stencil pattern?. Update alternatives. 2D Jacobi iteration. SOR and Red/Black SOR. + . Also Affiliated with NSF Center for High-Performance Reconfigurable Computing . This work was supported by National Science Foundation (NSF) grant CNS-0953447 . Tosiron Adegbija. 1. , Ann Gordon-Ross. “The Changing Music Industry”. By Eric Jurco. Structure of paper 3. Argumentative . 3 basic points, broke off into sub points. Persuasion with my statement rather than a narrative . Topic: . How the music industry is changing pop music, and how I have developed a more negative view towards the changes.. S. liding Mode Control. Application of an Auto-Tuning Neuron. to Sliding Mode Control. Wei-Der Chang, Rey-Chue Hwang, and Jer-Guang . Hsieh. IEEE . TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 32, NO. 4, NOVEMBER . + . Also Affiliated with NSF Center for High-Performance Reconfigurable Computing . Tosiron Adegbija. 1. , Ann Gordon-Ross. 1+. ,. . and . Arslan. Munir. 2. 1. Department . of Electrical and Computer . + . Also Affiliated with NSF Center for High-Performance Reconfigurable Computing . This work was supported by National Science Foundation (NSF) grant CNS-0953447 . Tosiron Adegbija and Ann Gordon-Ross. regarded in Australia for his tuning mastery, Eric has been evolving the performance of vehicles for over 15 years. He was passionate about cars from a very early age, and had an innate curiosity to Unique Auto Sales grew sales over the years and now runs an average of 1200 units through wholesale channels monthly. We are now forming an inventory sourcing division to help you fulfill your inventory needs. Distributed Auto-Tuning. Samuel . Williams, Leonid . Oliker. Jonathan Carter, John . Shalf. 1. Lawrence Berkeley National Laboratory. . SWWilliams@lbl.gov. Introduction. Management of . data locality and data movement .