Exploring DRAM organizations for energy-efficient and resilient exascale memories

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
探索DRAM组织的节能和弹性百亿亿级存储器
百亿亿次超级计算的功率目标是20MW,其中约30%预算用于内存子系统。商品dram将不满足这一要求。此外,所需的大量内存芯片(>10M)将导致严重的故障率。虽然专门的DRAM存储器已经通过3d堆叠或行缓冲调整大小来重新组织以降低功耗,但它们对容错性的影响尚未得到考虑。我们表明,解决可靠性和能量是一个涉及纠错成本、访问能量和刷新功率之间权衡的协同优化问题——减少物理页面大小以减少访问能量会增加错误恢复的能量/面积开销。此外,可以通过优化位行长度来降低功耗。根据NEK5000基准测试的模拟,提议的3d堆叠内存使用4kb的页面大小,消耗5.1pJ/bit。扩展到100PB时,内存在100PB/s时消耗4.7MW,虽然完全在总功率预算(20MW)之内,但也具有容错性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Distributed-memory parallel algorithms for generating massive scale-free networks using preferential attachment model Enabling comprehensive data-driven system management for large computational facilities There goes the neighborhood: Performance degradation due to nearby jobs A distributed dynamic load balancer for iterative applications Predicting application performance using supervised learning on communication features
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1