PPT-Breaking SIMD Shackles with an Exposed Flexible Microarchit

Venkatraman Govindaraju Tony Nowatzki Karthikeyan Sankaralingam University of WisconsinMadison 1 Motivation SIMD Single Instruction Multiple Data Exploits data level parallelism. 2 04 06 Nm Time UTC 123 4 m 123 4 Figure 2 Breaking scale 05 01 02 brk c 1 05 01 02 03 brk c 2 05 01 02 03 brk c 3 05 01 02 03 brk c 4 Figure 3 brPage 6br EZ EZ v 05 10 87226 10 87225 10 87224 10 87223 cc c m 87222 s Figure 4 Total breaking rate ZZ b Improving SIMD for 2015. Alastair McAlpine. Head of Local Outcomes Team. Office of Chief Statistician and Performance. Scottish Government. Facilitators. Cath Dickie. Ian Volante. Gregor Boyd. Victoria Kinnear-Lachhab. SIMD architectures. A data parallel architecture. Applying the same instruction to many data. Save control logic. A related architecture is the vector architecture. SIMD and vector architectures offer high performance for . Tailoring . SIMD Execution using . Heterogeneous Hardware . and Dynamic Configurability. Yongjun Park. 1. , Jason Jong Kyu Park. 1. , Hyunchul Park. 2. ,. and Scott Mahlke. 1. December. 3, 2012. 1. Alternative . securing devices for . 4-part . shackles. . . Member and client fully support use . of . split . pin for . shackles . on . permanent / dedicated . fixture points, . rigging . assemblies, items at height, . Tailoring . SIMD Execution using . Heterogeneous Hardware . and Dynamic Configurability. Yongjun Park. 1. , Jason Jong Kyu Park. 1. , Hyunchul Park. 2. ,. and Scott Mahlke. 1. December. 3, 2012. 1. Or, How much wood could a woodchuck chuck if a woodchuck could chuck . n. pieces of wood in parallel?. Wojtek Rajski, Nels Oscar, David Burri, Alex Diede. Introduction. We have seen how to improve performance through exploitation of:. Farzad Khorasani. , . Rajiv Gupta. , . Laxmi. N. . Bhuyan. University of California Riverside. Scalable SIMD-Efficient Graph Processing on GPUs. Graph Processing. Building blocks of data analytics.. GPU. Radeon. . R700. (HD . 4xxx. ) series. Elizabeth . Soechting. David Chang. Jessica Vasconcellos. 1. CS 433 . Advanced Computer Architecture. May 7. , 2008. General. 2. General. Interconnect. SIMD. Data-Level Parallel Architectures. Course 5MD00. Henk. . Corporaal. January 2015. h.corporaal@tue.nl. This lecture. Data-level parallel architectures. Vector machine. SIMD. (Single Instruction Multiple Data) processors. christiaan. gribble. karthik. . ramani. ieee. /. eurographics. symposium on interactive ray tracing. august 2008. early implementation. andrew. . kensler. (. utah. ). ingo. . wald. (. intel. ) . Farzad Khorasani. , . Rajiv Gupta. , . Laxmi. N. . Bhuyan. University of California Riverside. Scalable SIMD-Efficient Graph Processing on GPUs. Graph Processing. Building blocks of data analytics.. on OpenCL-based FPGAs. Zeke Wang. , Johns Paul, Hui Yan Cheah . (. NTU,. . Singapore), . Bingsheng He (. NUS,. Singapore), . Wei Zhang (HKUST, Hong Kong). 1. Outline. Background and Problem. Challenges. Evgeni. . Krimer. (UT Austin). Patrick Chiang (Oregon State). Mattan Erez (UT Austin). All systems . power/energy . bound. The good:. Transistor still following Moore’s Law. The . bad:. Transistor power efficiency improving too slowly.