Bharan Giridhar, Michael Cieslak, Deepankar Duggal, R. Dreslinski, H. Chen, R. Patti, B. Hold, C. Chakrabarti, T. Mudge, D. Blaauw
{"title":"Exploring DRAM organizations for energy-efficient and resilient exascale memories","authors":"Bharan Giridhar, Michael Cieslak, Deepankar Duggal, R. Dreslinski, H. Chen, R. Patti, B. Hold, C. Chakrabarti, T. Mudge, D. Blaauw","doi":"10.1145/2503210.2503215","DOIUrl":null,"url":null,"abstract":"The power target for exascale supercomputing is 20MW, with about 30% budgeted for the memory subsystem. Commodity DRAMs will not satisfy this requirement. Additionally, the large number of memory chips (>10M) required will result in crippling failure rates. Although specialized DRAM memories have been reorganized to reduce power through 3D-stacking or row buffer resizing, their implications on fault tolerance have not been considered. We show that addressing reliability and energy is a co-optimization problem involving tradeoffs between error correction cost, access energy and refresh power-reducing the physical page size to decrease access energy increases the energy/area overhead of error resilience. Additionally, power can be reduced by optimizing bitline lengths. The proposed 3D-stacked memory uses a page size of 4kb and consumes 5.1pJ/bit based on simulations with NEK5000 benchmarks. Scaling to 100PB, the memory consumes 4.7MW at 100PB/s which, while well within the total power budget (20MW), is also error-resilient.","PeriodicalId":371074,"journal":{"name":"2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"87","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2503210.2503215","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 87
Abstract
The power target for exascale supercomputing is 20MW, with about 30% budgeted for the memory subsystem. Commodity DRAMs will not satisfy this requirement. Additionally, the large number of memory chips (>10M) required will result in crippling failure rates. Although specialized DRAM memories have been reorganized to reduce power through 3D-stacking or row buffer resizing, their implications on fault tolerance have not been considered. We show that addressing reliability and energy is a co-optimization problem involving tradeoffs between error correction cost, access energy and refresh power-reducing the physical page size to decrease access energy increases the energy/area overhead of error resilience. Additionally, power can be reduced by optimizing bitline lengths. The proposed 3D-stacked memory uses a page size of 4kb and consumes 5.1pJ/bit based on simulations with NEK5000 benchmarks. Scaling to 100PB, the memory consumes 4.7MW at 100PB/s which, while well within the total power budget (20MW), is also error-resilient.