PPT-Approximating Warps with Intra-warp Operand

Value Similarity Daniel Wong Nam Sung Kim Murali Annavaram University of California Riverside dwongeceucredu University of Illinois Urbana Champagin. Ankit Sethia. 1. , Ganesh Dasika. 2. , . Mehrzad. Samadi. 1. ,. Scott Mahlke. 1. 1-University of Michigan. 2-ARM R&D Austin. 1. Introduction. 2. High Throughput – 1 . TeraFlop. . High Energy Efficiency. Approximating the . Depth. via Sampling and Emptiness. Approximating the . Depth. via Sampling and Emptiness. Approximating the . Depth. via Sampling and Emptiness. Example: Range tree. S = Set of points in the plane. Equilibria. : How…. …and What’s the Point?. Yevgeniy Vorobeychik. Sandia National Laboratories. Who am I?. Ph.D. CS, University of Michigan, advised by Michael Wellman. approximating/estimating . ©Chad Kersey and Sudhakar Yalamanchili unless otherwise noted. Objectives. Detailed look at the implementation of a SIMT GPU. Example of the type of information propagated down the pipeline. Basis for the next assignment and the default project. : Speeding up GPU Warps . by . Reducing Memory . Pitstops. Ankit Sethia* D. . Anoushe. Scott Mahlke. . Jamshidi. University of Michigan. Graphics. Simulation . Linear Algebra. Data Analytics. Instructor Notes. This lecture deals with how work groups are scheduled for execution on the compute units of devices. Also explain the effects of divergence of work items within a group and its negative effect on performance. Shirly. . Yakubov. Motivation. Given a set . S. of n objects we . want to store them in . a . data-structure . that . could answer . range queries. For a range . r. we have. :. range-searching counting. Ankit Sethia. 1. , Ganesh Dasika. 2. , . Mehrzad. Samadi. 1. ,. Scott Mahlke. 1. 1-University of Michigan. 2-ARM R&D Austin. 1. Introduction. 2. High Throughput – 1 . TeraFlop. . High Energy Efficiency. Goal. Idle thread. Active thread. Compaction. Compact threads in a warp to coalesce (and eliminate) idle cycles .  improve utilization. References. V. . Narasiman. , et. al., “Improving GPU Performance via Large Warps and Two-Level Scheduling,” MICRO 2011. T. Rogers, M O’Conner, and T. . Aamodt. MICRO 2012. Goal. Understand the relationship between schedulers (warp/wavefront) and locality behaviors . Distinguish between inter-wavefront and intra-wavefront locality. ©Chad Kersey and Sudhakar Yalamanchili unless otherwise noted. Objectives. Detailed look at the implementation of a SIMT GPU. Example of the type of information propagated down the pipeline. Basis for the next assignment and the default project. Lecture 5: GPU Compute . Architecture. 1. Last time.... GPU Memory System. Different kinds of memory pools, caches, . etc. Different optimization techniques. 2. Warp Schedulers. Warp schedulers find a warp that is ready to execute its next instruction and available execution cores and then start execution. Shirly. . Yakubov. Motivation. Given a set . S. of n objects we . want to store them in . a . data-structure . that . could answer . range queries. For a range . r. we have. :. range-searching counting. Characterization. , . Impact. , and . Mitigation. Ping Xiang. ,. . Yi Yang,. . Huiyang. . Zhou. 1. The 20th IEEE International Symposium On High Performance Computer Architecture. , Orlando, Florida, .