PPT-Continuous Runahead: Transparent Hardware Acceleration for Memory Intensive Workloads

Milad Hashemi Onur Mutlu and Yale N Patt 1 Runahead requests are overwhelmingly accurate 2 Runahead has very low prefetch coverage 3 Runahead intervals are short dramatically limiting runahead performance gain. NFV . PoC. (Proof of Concept) #21. http://nfvwiki.etsi.org/index.php?title=Network_Intensive_and_Compute_Intensive_Hardware_Acceleration. peter.ashwoodsmith@huawei.com. (presenter). evelyne.roch@huawei.com. Ankit Sethia* Scott . Mahlke. University of Michigan. Graphics. Simulation . Linear Algebra. Data Analytics. Machine Learning. Computer Vision. Resource Requirements of GPU applications are diverging. Jen-Cheng Huang. 1. Matteo Monchiero. 2. Yoshio Turner. 2. Georgia Institute of Technology. 1 . Hewlett-Packard Labs. 2. . Data center management tasks need . Stacked DRAM as . P. art . o. f . M. emory . Jaewoong Sim . Alaa. R. . Alameldeen. . Zeshan. Chishti. Chris Wilkerson Hyesoon Kim. MICRO-47 | December 2014. Stacked DRAM. DRAM Cache. FAST. Memory. SoC. Platform. Hao. Wang. University of Wisconsin, Madison. Outline. Introduction on . SoC. Motivation. Verilog implementation of JPEG encoder. Integrated . SoC. simulator. Future work. System-on-Chip Platform. HARDWARE AND SOFTWARE BASICS. Copyright © . 2015 . McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.. Overview. Milad Hashemi, Onur . Mutlu. , and Yale N. . Patt. 1) . Runahead requests are overwhelmingly accurate:. 2) . Runahead has very low . prefetch. coverage:. 3) . Runahead intervals are short, dramatically limiting runahead performance gain:. Smruti. R. Sarangi. Data Prefetching. Instead of . instructions . let us now . prefetch. . data. Important distinction. Instructions. are fetched into the in-order part of the OOO pipeline. Data. is fetched into the OOO pipeline. Chapter 1 – Introduction (Pgs 3 – 42). Architecture Review. SGG see a computer system as:. Hardware : CPU, Memory, I/O, . communication. . *Operating System* - The rest of the course. Applications: Programs that do stuff (. Hardware vs. Software. Hardware includes. CPU = central processing unit. Memory = RAM (random access memory). Input = Keyboard, mouse, microphone. Output = Screen, speaker, printer. Storage = Hard drive, DVD, Solid State. Mapping (TOM). Enabling . Programmer-Transparent . Near-Data Processing in GPU Systems. Kevin Hsieh. Eiman. . Ebrahimi. , . Gwangsun. Kim, . Niladrish. Chatterjee, . Mike O’Connor, . Nandita. . CSE351 Autumn2011. 1. st. Lecture, September 28. Instructor:. . Luis Ceze. Teaching Assistants:. Nick Hunt, Michelle Lim, Aryan . Naraghi. , Rachel . Sobel. 1. 2. Who is Luis?. PhD in architecture, . Enabling Programmer-Transparent . Near-Data Processing in GPU Systems. Kevin Hsieh. Eiman. . Ebrahimi. , . Gwangsun. Kim, . Niladrish. Chatterjee, . Mike O’Connor, . Nandita. . Vijaykumar. , . Onur. Faculty Professional Learning Series: Webinar #4. December 1, 2020. Lindsey Hayes, M.Ed., & Amy Colpo, M.P.P., American Institutes for Research. Lynn Holdheide, M.Ed., CEEDAR Center. Cara McDermott-.