PDF-Fundamental Latency Tradeoffs in Architecting DRAM Cache Outperforming Impractical SRAMTags

Author : kittie-lecroy | Published Date : 2014-12-12

Qureshi Gabriel H Loh Dept of Electrical and Computer Engineering AMD Research Georgia Institute of Technology Advanced Micro Devices In c moingatechedu gabelohamdcom

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Fundamental Latency Tradeoffs in Architecting DRAM Cache Outperforming Impractical SRAMTags: Transcript

Qureshi Gabriel H Loh Dept of Electrical and Computer Engineering AMD Research Georgia Institute of Technology Advanced Micro Devices In c moingatechedu gabelohamdcom Abstract This paper analyzes the design tradeoffs in architecting largescale DRAM. Qureshi Gabriel H Loh Dept of Electrical and Computer Engineering AMD Research Georgia Institute of Technology Advanced Micro Devices In c moingatechedu gabelohamdcom Abstract This paper analyzes the design tradeoffs in architecting largescale DRAM Niladrish. . Chatterjee. Manjunath. . Shevgoor. Rajeev . Balasubramonian. Al Davis. Zhen Fang. ‡†. Ramesh . Illikkal. *. Ravi . Iyer. *. University of Utah , NVidia. ‡. and Intel Labs*. †. for 3D memory systems. CAMEO. 12/15/2014 MICRO. Cambridge, UK. Chiachen Chou, Georgia Tech. Aamer. . Jaleel. , Intel. Moinuddin. K. . Qureshi. , Georgia Tech. Executive Summary. How to use . S. tacked DRAM: Cache or Memory. K. . Qureshi. ECE, Georgia Tech. ISCA 2012 . Michele . Franceschini. , . Ashish. . Jagmohan. , Luis . Lastras. . IBM T. J. Watson Research Center. PreSET. : . Improving PCM performance. b. y exploiting asymmetry in write times. Bank Privatization for Predictability and Temporal Isolation. Sungjun. . Kim . Columbia . University. Edward A. Lee . UC . Berkeley . Isaac . Liu . UC Berkeley. Hiren. D. Patel University of Waterloo. Jaewoong. . Sim. *, Gabriel H. Loh. +. , Vilas Sridharan. #. , Mike O’Connor. +. June . 2013 . *Georgia Tech. +. AMD Research. #. AMD RAS Architecture. Die-stacked Memory. Die-stacking is coming along, esp. DRAM. Niladrish. . Chatterjee. Manjunath. . Shevgoor. Rajeev . Balasubramonian. Al Davis. Zhen Fang. ‡†. Ramesh . Illikkal. *. Ravi . Iyer. *. University of Utah , NVidia. ‡. and Intel Labs*. †. ISCA 2012 . Michele . Franceschini. , . Ashish. . Jagmohan. , Luis . Lastras. . IBM T. J. Watson Research Center. PreSET. : . Improving PCM performance. b. y exploiting asymmetry in write times. Niladrish Chatterjee. Mike O’Connor. Gabriel H. . Loh. Nuwan. . Jayasena. Rajeev . Balasubramonian. Irregular GPGPU Applications. Conventional GPGPU workloads access vector or matrix-based data structures. Lecture 6 - Memory. Dr. George Michelogiannakis. EECS, University of California at Berkeley. CRD, Lawrence Berkeley National Laboratory. http://. inst.eecs.berkeley.edu. /~cs152. CS152 . Administritivia. and the Implications for Performance Optimization. Samuel Williams. 1,2. Jonathan Carter. 2. , Richard Vuduc. 3. , Leonid Oliker. 1,2. , John Shalf. 2. , . Katherine Yelick. 1,2. , James Demmel. 1,2. by Exploiting the Variation in Local . Bitlines. . Jeremie S. Kim. . Minesh. Patel . Hasan Hassan . Onur. . Mutlu. . Motivation. : DRAM latency is a . major performance bottleneck. Problem. Language-Directed Hardware Design for Network Performance Monitoring Srinivas Narayana, Anirudh Sivaraman , Vikram Nathan, Prateesh Goyal, Venkat Arun , Mohammad Alizadeh , Vimal Jeyakumar Vinson Young. Prashant Nair. Moinuddin Qureshi. 1. MOORE’s LAW HITS BANDWIDTH WALL. 2. Moore’s scaling encounters Bandwidth Wall. 3D-DRAM MITIGATES BANDWIDTH WALL. 3. 3D-DRAM. Hybrid Memory Cube (HMC) from Micron, .

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