PPT-Data Prefetching

Smruti R Sarangi Data Prefetching Instead of instructions let us now prefetch data Important distinction Instructions are fetched into the inorder part of the OOO pipeline Data is fetched into the OOO pipeline. And a great value too! We understand the importance of your data and the value of a full, fast and secure recovery. At Data Rescue MDs, our motto is “Lose your fear, not your data!” We demonstrate our data recovery commitment to our customers every day working tirelessly to successfully rescue their data. Our data recovery engineers are HIPAA certified to appropriately manage sensitive data throughout our secure data recovery process. Hugo Patterson Garth A Gibson Eka Ginting Daniel Stodolsky Jim Zelenka Department of Electrical and Computer Engineering School of Computer Science Carnegie Mellon University Pittsburgh PA 152133890 rhp garth eginting danner jimzcscmu 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. Cache . III. Steve Ko. Computer Sciences and Engineering. University at Buffalo. 2. Last . time…. Basic cache architecture. Placement policy. Replacement policy. Average memory access time =. hit time + miss rate * miss penalty. CMP: 2 or 4 cores, 3.2GHz ROB size: 128 Fetch/Exec/Retire/Commit width: 4 / 7 / 5 / 3 Branch predictor: G-share, 64KB, 4K BTB Branch misprediction penalty: 10 cycles Processor side prefetcher: Stride Adwait Jog. , Onur Kayiran, Asit Mishra, Mahmut Kandemir, Onur Mutlu, Ravi Iyer, Chita Das . Multi-threading. Caching. Prefetching . Main Memory. . Improve. . Replacement . Policies. Parallelize your code!. Shared-Resource Management. for Multi-Core Systems. Eiman Ebrahimi. *. Chang . Joo. . Lee. *. +. Onur. . Mutlu. ‡. Yale N. . Patt. *. * HPS Research Group . The University of Texas at Austin. Enhancement . for Accurate Stream Prefetching. Gang Liu. 1. , . Zhuo. Huang. 1. , . Jih. -Kwon Peir. 1. , . Xudong. Shi. 2. , Lu Peng. 3. 1. University of Florida . 2. Google Inc. 3. Louisiana . State . Predictor . Virtualization. Andreas . Moshovos. Univ. of Toronto. Ioana . Burcea’s. . Thesis work. Some parts joint with Stephen . Somogyi. (CMU) and . Babak. . Falsafi. (EPFL). 2. Prediction . Computer Systems. Intel Collaborative Research Institute. Computational Intelligence. Yoav. . Etsion. , . Technion. CS & EE. Dan . Tsafrir. , . Technion. CS. Shie. . Mannor. , . Technion. EE. Tekin. . Bicer. , . Jian. Yin, David Chiu, . Gagan. . Agrawal. . and Karen . Schuchardt. Ohio State University. Washington State University. Pacific Northwest National Laboratories. 1. †. †. 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. Results and Awards. Seth . Pugsley. Thanks!. Big thanks to Hyesoon Kim and the Program Committee. Babak. . Falsafi. Mike . Ferdman. . Aamer. Jaleel. Daniel Jiménez Calvin Lin . Moin. Qureshi. Last Level Collective Hardware Prefetching For Data-Parallel Applications George Michelogiannakis, John Shalf Lawrence Berkeley National Laboratory, Berkeley, CA, USA HiPC 2017 Overview Last-level cache (LLC) prefetcher that exploits