PPT-3: Motivations Reducing DRAM Latency via

Author : cappi | Published Date : 2022-06-15

ChargeLevelAware LookAhead Partial Restoration Yaohua Wang 12 Arash Tavakkol 1 Lois Orosa 14 Saugata Ghose 3 Nika Mansouri Ghiasi 1 Minesh Patel

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3: Motivations Reducing DRAM Latency via: Transcript

ChargeLevelAware LookAhead Partial Restoration Yaohua Wang 12 Arash Tavakkol 1 Lois Orosa 14 Saugata Ghose 3 Nika Mansouri Ghiasi 1 Minesh Patel. by Parallelizing Refreshes. with Accesses. Donghyuk. Lee, . Zeshan. . Chishti. , . Alaa. . Alameldeen. , Chris Wilkerson, . Yoongu. Kim, . Onur. . Mutlu. Kevin Chang. Executive Summary. DRAM . refresh interferes with memory accesses. Niladrish. . Chatterjee. Manjunath. . Shevgoor. Rajeev . Balasubramonian. Al Davis. Zhen Fang. ‡†. Ramesh . Illikkal. *. Ravi . Iyer. *. University of Utah , NVidia. ‡. and Intel Labs*. †. Ph.D. Thesis Proposal. Kshitij Sudan. Thesis Statement. Improving DRAM access latency, power consumption, and capacity by leveraging intelligent data placement.. Overview. CPU. MC. DIMM. …. Penn State . DAC’12, ISPASS’13. Architecture Reading Club Spring'13. 1. Cache-Revive: Architecting Volatile STT-RAM Caches for Enhanced Performance in CMPs. Penn State . DAC’12. Architecture Reading Club Spring'13. Niladrish. . Chatterjee. Manjunath. . Shevgoor. Rajeev . Balasubramonian. Al Davis. Zhen Fang. ‡†. Ramesh . Illikkal. *. Ravi . Iyer. *. University of Utah , NVidia. ‡. and Intel Labs*. †. Kshitij. Sudan. , . Niladrish. . Chatterjee. , David . Nellans. , Manu . Awasthi. , Rajeev . Balasubramonian. , Al Davis. School of Computing, University of Utah. ASPLOS-2010. DRAM Memory Constraints. DRAM Scaling. Prof. Onur Mutlu. http://www.ece.cmu.edu/~omutlu. onur@cmu.edu. HiPEAC. ACACES Summer School 2013. July 15-19, . 2013. The Main Memory System. Main memory is a critical component of all computing systems. Before we begin our lesson for today, pull out your T-Chart from yesterday. . Draw a line underneath your main characters characteristics (Internal and External).. Begin the process again with any minor characters you will have in your story. Give them a name and their own traits!. OLTP on NVM:. YMMV. The Last Six Months. ?. PDL Retreat. October 2013. PDL Visit Day. May 2014. Prison Life. Washing Dishes. Not Fighting. Repentant. Cafeteria Thievery. Shankings. Making . Pruno. GOOD. OLTP on the NVM SDV:. YMMV. 2013. January. Retreat. Thesis. Defense. December. Retreat. Job. Interviews. Moved to. CMU. 2013. January. Retreat. Thesis. Defense. December. Retreat. Job. Interviews. Moved to. Ashok Anand. , . Chitra. . Muthukrishnan. , Steven . Kappes. , and . Aditya. . Akella. University of Wisconsin-Madison. . Suman. . Nath. Microsoft Research. New data-intensive networked systems. Large hash tables (10s to 100s of GBs). 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. Access Locality. Hasan Hassan,. Gennady . Pekhimenko. , . Nandita Vijaykumar, . Vivek. . Seshadri. , Donghyuk Lee, Oguz Ergin, . Onur. . Mutlu. Executive Summary. Goal. : . Reduce average DRAM access latency with no modification to the existing DRAM chips.

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