PDF-Low-latency main memory compression framework

thatcansignicantlydegradeperformanceTocounterthisproblempriorwork32539oncachecompressionproposedspecializedcompressionalgorithmsthatexploitregularpatternspresentininmemorydataandshowedthats. Xtreme Compression, Inc. is a software design consultancy with special expertise in structured data compression. We apply our proprietary algorithms and source code to compress and encrypt structured data whenever performance beyond the reach of conventional methods is required. g a core dump Semiconductors are almost universal today Memory Cells Properties Exhibit two stable or semi stable states representing 1 and 0 Capable of being written to at least once to set state Capable of being read to sense the state Memory Cell Niladrish. . Chatterjee. Manjunath. . Shevgoor. Rajeev . Balasubramonian. Al Davis. Zhen Fang. ‡†. Ramesh . Illikkal. *. Ravi . Iyer. *. University of Utah , NVidia. ‡. and Intel Labs*. †. : . A column-oriented DBMS. Ryan Johnson. CSC2531. The memory wall has arrived. CPU performance. +70%/year. Memory performance. latency: -50%/. decade. bandwidth: +20%/year (est.). Why?. DRAM focus on capacity (+70%/year). A Main Memory . Compression Framework with . Low Complexity and Low Latency . Gennady Pekhimenko. , . Vivek. . Seshadri. . , . Yoongu. Kim, . Hongyi. . Xin. , . Onur. . Mutlu. , . Todd C. . Mowry. Network Interfaces . for In-Memory Rack-Scale Computing. Alexandros Daglis. ,. . Stanko. . Novakovic. , . Edouard. . Bugnion. , . Babak. . Falsafi. , Boris Grot. In-Memory Computing for High Performance. Tekin. . Bicer. , . Jian. Yin, David Chiu, . Gagan. . Agrawal. . and Karen . Schuchardt. Ohio State University. Washington State University. Pacific Northwest National Laboratories. 1. †. †. openacc. Ebad. . Salehi. , Ahmad . Lashgar. and . Amirali. . Baniasadi. Electrical and Computer Engineering Department. University of Victoria. This Work. Motivation: Effective memory bandwidth usage is needed to achieve high performance in GPUs.. David Hay . With . Anat Bremler-Barr, Daniel . Krauthgamer. , . Shimrit. . Tzur. David. This research was supported by ERC starting grant 259805. URL Matching. 2. Action . URL. . A1. work. .. com. Practical Data Compression . for On-Chip Caches. Gennady Pekhimenko. . Vivek. . Seshadri. . . Onur. . Mutlu. . , Todd C. . Mowry. . . Phillip B. Gibbons* . Michael A. . Kozuch. *. *. Executive Summary. with Application-Transparent Support . for Multiple Page Sizes. Rachata. . Ausavarungniru. n. ,. . Joshua Landgraf, Vance Miller. Saugata. . Ghose. , . Jayneel. Gandhi, Christopher J. . Rossbach. with Low Complexity and Low Latency . Gennady Pekhimenko, Advisers: Todd C. Mowry and Onur Mutlu (Carnegie Mellon University). Executive Summary. Main memory is a limited shared resource. . Observation. Lecture . 33: . Interconnection Networks. Prof. Onur Mutlu. Carnegie Mellon University. Spring . 2015, . 4/. 27/2015. Logistics. Lab . 7 and Lab 8. Final Exam. Midterm II scores. Course grades so far. Stephan Meier. Some slides authored by Tyler Huberty, Onur Mutlu (used with permission). Prefetching. Outline. Outline. Motivation. Instruction prefetching. Data prefetching. Research directions. A few definitions before we start….