Towards near-threshold server processors
dc.contributor.author | Pahlevan, Ali | |
dc.contributor.author | Picorel, Javier | |
dc.contributor.author | Zarandi, Arash Pourhabibi | |
dc.contributor.author | Rossi, Davide | |
dc.contributor.author | Zapater Sancho, Marina | |
dc.contributor.author | Bartolini, Andrea | |
dc.contributor.author | Del Valle, Pablo G. | |
dc.contributor.author | Atienza, David | |
dc.contributor.author | Benini, Luca | |
dc.contributor.author | Falsafi, Babak | |
dc.date.accessioned | 2023-06-18T06:58:17Z | |
dc.date.available | 2023-06-18T06:58:17Z | |
dc.date.issued | 2016-03-14 | |
dc.description | © Copyright 2016 IEEE. Design, Automation and Test in Europe Conference and Exhibition (DATE) (2016. Dresden, Germany). This work has been partially supported by the YINS RTD project (no. 20NA21 150939), funded by Nano-Tera.ch with Swiss Confederation Financing and scientifically evaluated by SNSF, the EC FP7 GreenDataNet STREP project (Agreement No. 609000), the EuroLab-4-HPC project, the FP7 ERC Advance project MULTITHERMAN (g.a. 291125), the H2020 FETHPC ExaNoDe (g.a. 671578), a research scholarship by Universidad Politécnica de Madrid, and by the Spanish Ministry of Economy and Competitiveness, under contract TEC2012-33892. | |
dc.description.abstract | The popularity of cloud computing has led to a dramatic increase in the number of data centers in the world. The ever-increasing computational demands along with the slowdown in technology scaling has ushered an era of power-limited servers. Techniques such as near-threshold computing (NTC) can be used to improve energy efficiency in the post-Dennard scaling era. This paper describes an architecture based on the FD-SOI process technology for near-threshold operation in servers. Our work explores the trade-offs in energy and performance when running a wide range of applications found in private and public clouds, ranging from traditional scale-out applications, such as web search or media streaming, to virtualized banking applications. Our study demonstrates the benefits of near-threshold operation and proposes several directions to synergistically increase the energy proportionality of a near-threshold server. | |
dc.description.department | Depto. de Estructura de la Materia, Física Térmica y Electrónica | |
dc.description.faculty | Fac. de Ciencias Físicas | |
dc.description.refereed | TRUE | |
dc.description.sponsorship | Unión Europea. FP7 | |
dc.description.sponsorship | Unión Europea. H2020 | |
dc.description.sponsorship | Ministerio de Economía y Competitividad (MINECO) | |
dc.description.sponsorship | YINS RTD | |
dc.description.sponsorship | Nano-Tera.ch | |
dc.description.sponsorship | Swiss Confederation Financing | |
dc.description.sponsorship | Universidad Politécnica de Madrid | |
dc.description.status | pub | |
dc.eprint.id | https://eprints.ucm.es/id/eprint/39952 | |
dc.identifier.doi | 10.3850/9783981537079_0980 | |
dc.identifier.issn | 1530-1591 | |
dc.identifier.officialurl | https://doi.org/10.3850/9783981537079_0980 | |
dc.identifier.relatedurl | http://ieeexplore.ieee.org/ | |
dc.identifier.relatedurl | https://www.researchgate.net/ | |
dc.identifier.uri | https://hdl.handle.net/20.500.14352/24697 | |
dc.journal.title | Proceeding of the Design, Automation & Test in Europe Conference & Exhibition (DATE), 2016 | |
dc.language.iso | eng | |
dc.page.final | 12 | |
dc.page.initial | 7 | |
dc.publisher | IEEE | |
dc.relation.projectID | MULTITHERMAN (291125) | |
dc.relation.projectID | GreenDataNet (609000) | |
dc.relation.projectID | ExaNoDe (671578) | |
dc.relation.projectID | TEC2012-33892 | |
dc.relation.projectID | 20NA21 150939 | |
dc.relation.projectID | EuroLab-4-HPC | |
dc.rights.accessRights | open access | |
dc.subject.cdu | 537 | |
dc.subject.keyword | Power aware computing | |
dc.subject.keyword | Cloud computing | |
dc.subject.keyword | Computer centres | |
dc.subject.keyword | File servers. | |
dc.subject.ucm | Electricidad | |
dc.subject.ucm | Electrónica (Física) | |
dc.subject.unesco | 2202.03 Electricidad | |
dc.title | Towards near-threshold server processors | |
dc.type | journal article | |
dc.volume.number | 2016 | |
dcterms.references | [1] J. Koomey, “Growth in data center electricity use 2005 to 2010,” Analytics Press, Oakland, CA, Tech. Rep., 2011. [2] R. Dreslinski, M. Wieckowski, D. Blaauw, D. Sylvester, and T. Mudge, “Near-threshold computing: Reclaiming moore’s law through energy efficient integrated circuits,” Proceedings of the IEEE, vol. 98, no. 2, pp. 253–266, 2010. [3] Markovic, et al., “Ultralow-power design in near-threshold region,” Proceedings of the IEEE, 2010. [4] N. Planes, et al., “28nm fdsoi technology platform for high-speed lowvoltage digital applications,” in IEEE VLSIT, 2012. [5] D. Jacquet, et al., “A 3 ghz dual core processor ARM cortex TM -a9 in 28 nm UTBB FD-SOI CMOS with ultra-wide voltage range and energy efficiency optimization,” J. Solid-State Circuits, 2014. [6] D. Rossi, et al., “A 60 gops/w, -1.8 v to 0.9 v body bias ULP cluster in 28 nm UTBB fd-soi technology,” Solid-State Electronics, 2015. [7] M. Ferdman, A. Adileh, Y. O. Koc¸berber, S. Volos, M. Alisafaee, D. Jevdjic, C. Kaynak, A. D. Popescu, A. Ailamaki, and B. Falsafi, “A case for specialized processors for scale-out workloads,” IEEE Micro, vol. 34, no. 3, pp. 31–42, 2014. [8] L. Gwennap, “Thunderx rattles server market,” Microprocessor Report, vol. 29, no. 6, pp. 1–4, 2014. [9] P. Lotfi-Kamran, et al., “Scale-out processors,” in ACM/IEEE ISCA, 2012. [10] S. Li, et al., “McPAT: an integrated power, area, and timing modeling framework for multicore and manycore architectures,” in IEEE/ACM, MICRO, 2009. [11] Micron, “4Gb: x4, x8, x16 DDR4 SDRAM features,” https://www.micron.com/∼/media/documents/products/data-sheet/dram/ddr4/4gb ddr4 sdram.pdf. [12] S. Li, et al., “CACTI-P: architecture-level modeling for sram-based structures with advanced leakage reduction techniques,” in IEEE/ACM ICCAD, 2011. [13] N. Muralimanohar, et al., “Optimizing NUCA organizations and wiring alternatives for large caches with CACTI 6.0,” in IEEE/ACM MICRO, 2007. [14] S. Volos, et al., “Bump: Bulk memory access prediction and streaming,” in IEEE/ACM MICRO, 2014. [15] Micron, “DDR4 SDRAM system-power calculator,” https://www.micron.com/∼/media/documents/products/power-calculator/ddr4 power calc.xlsm. [16] M. Ferdman, et al., “Clearing the clouds: a study of emerging scale-out workloads on modern hardware,” in ACM ASPLOS, 2012. [17] S. Shen, V. van Beek, and A. Iosup, “Statistical characterization of business-critical workloads hosted in cloud datacenters,” in IEEE/ACM CCGrid, 2015. [18] D. Lo, et al., “Towards energy proportionality for large-scale latencycritical workloads,” in ACM/IEEE ISCA, 2014. [19] D. Meisner, et al., “Power management of online data-intensive services,” in ACM/IEEE ISCA, 2011. [20] V. D. Jerger, N.E. and M. Lipasti, “An evaluation of server consolidation workloads for multi-core designs,” in IISWC, 2007. [21] J. Mars, et al., “Bubble-Up: increasing utilization in modern warehouse scale computers via sensible co-locations,” in IEEE/ACM MICRO, 2011. [22] C. K. Christina Delimitro, “Optimizing resource provisioning in shared cloud systems,” Standford University, Tech. Rep., 2014. [23] T. F. Wenisch, R. E. Wunderlich, M. Ferdman, A. Ailamaki, B. Falsafi, and J. C. Hoe, “Simflex: Statistical sampling of computer system simulation,” IEEE Micro, vol. 26, no. 4, pp. 18–31, 2006. [24] E. C. Paul Rosenfeld and B. Jacob, “DRAMSim2: A cycle accurate memory system simulator,” Computer Architecture Letters, 2011. [25] R. E. Wunderlich, et al., “SMARTS: accelerating microarchitecture simulation via rigorous statistical sampling,” in ACM/IEEE ISCA, 2003. [26] “Intel Core i7-4785T Processor (8M Cache, up to 3.20 Ghz) Specifications,” http://ark.intel.com/products/80814. [27] J. Dean and L. A. Barroso, “The tail at scale,” Communications of the ACM, 2013. [28] B. F. Cooper, et al., “Benchmarking cloud serving systems with ycsb,” in ACM SoCC, 2010. [29] L. A. Barroso, J. Clidaras, and U. Hölzle, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Second Edition, ser. Synthesis Lectures on Computer Architecture. Morgan & Claypool Publishers, 2013. [30] L. A. Barroso and U. Hölzle, “The case for energy-proportional computing,” IEEE Computer, vol. 40, no. 12, pp. 33–37, 2007. [31] K. T. Malladi, et al., “Towards energy-proportional datacenter memory with mobile DRAM,” in ACM/IEEE ISCA, 2012. | |
dspace.entity.type | Publication |
Download
Original bundle
1 - 1 of 1