Power and Energy Characterization of an Open Source 25-core Manycore Processor

被引:25
作者
McKeown, Michael [1 ]
Lavrov, Alexey [1 ]
Shahrad, Mohammad [1 ]
Jackson, Paul J. [1 ]
Fu, Yaosheng [1 ,2 ]
Balkind, Jonathan [1 ]
Nguyen, Tri M. [1 ]
Lim, Katie [1 ]
Zhou, Yanqi [1 ,3 ]
Wentzlaff, David [1 ]
机构
[1] Princeton Univ, Princeton, NJ 08544 USA
[2] NVIDIA, Santa Clara, CA USA
[3] Baidu, Beijing, Peoples R China
来源
2018 24TH IEEE INTERNATIONAL SYMPOSIUM ON HIGH PERFORMANCE COMPUTER ARCHITECTURE (HPCA) | 2018年
基金
美国国家科学基金会;
关键词
processor; manycore; power; energy; thermal; characterization; DARK SILICON; DESIGN; MICROPROCESSOR; CIRCUITS;
D O I
10.1109/HPCA.2018.00070
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The end of Dennard's scaling and the looming power wall have made power and energy primary design goals for modern processors. Further, new applications such as cloud computing and Internet of Things (IoT) continue to necessitate increased performance and energy efficiency. Manycore processors show potential in addressing some of these issues. However, there is little detailed power and energy data on manycore processors. In this work, we carefully study detailed power and energy characteristics of Piton, a 25-core modern open source academic processor, including voltage versus frequency scaling, energy per instruction (EPI), memory system energy, network-on-chip (NoC) energy, thermal characteristics, and application performance and power consumption. This is the first detailed power and energy characterization of an open source manycore design implemented in silicon. The open source nature of the processor provides increased value, enabling detailed characterization verified against simulation and the ability to correlate results with the design and register transfer level (RTL) model. Additionally, this enables other researchers to utilize this work to build new power models, devise new research directions, and perform accurate power and energy research using the open source processor. The characterization data reveals a number of interesting insights, including that operand values have a large impact on EPI, recomputing data can be more energy efficient than loading it from memory, on-chip data transmission (NoC) energy is low, and insights on energy efficient multithreaded core design. All data collected and the hardware infrastructure used is open source and available for download at http://www.openpiton.org.
引用
收藏
页码:762 / 775
页数:14
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