EcoG: A Power-Efficient GPU Cluster Architecture for Scientific Computing

2011 ◽  
Vol 13 (2) ◽  
pp. 83-87 ◽  
Author(s):  
Mike Showerman ◽  
Jeremy Enos ◽  
Craig Steffen ◽  
Sean Treichler ◽  
William Gropp ◽  
...  
Keyword(s):  
Scientific Computing ◽  
Gpu Cluster ◽  
Power Efficient ◽  
Cluster Architecture

scholarly journals RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization

Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1799 ◽  
Author(s):  
Yuling Fang ◽  
Qingkui Chen ◽  
Neal N. Xiong ◽  
Deyu Zhao ◽  
Jingjuan Wang
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Energy Optimization ◽  
Gpu Cluster ◽  
Cluster Architecture ◽  
Effective Performance

scholarly journals RGCA: a Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization

2017 ◽  
Author(s):  
Yuling Fang ◽  
Qingkui Chen ◽  
Neal N. Xiong ◽  
Deyu Zhao ◽  
Jingjuan Wang
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
High Performance ◽  
Large Scale ◽  
High Reliability ◽  
Low Cost ◽  
Computing System ◽  
Gpu Cluster ◽  
Cluster Architecture ◽  
Level Parallelism

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things computing. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSN. Then, using the CUDA Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

scholarly journals Analysis of GPU Power Consumption Using Internal Sensors

2017 ◽  
Author(s):  
Mariza Ferro ◽  
André Yokoyama ◽  
Vinicius Klõh ◽  
Gabrieli Silva ◽  
Rodrigo Gandra ◽  
...  
Keyword(s):  
High Resolution ◽  
Power Consumption ◽  
High Performance ◽  
Scientific Computing ◽  
Power Cost ◽  
Power Efficient ◽  
Resolution Power ◽  
Power Profile ◽  
High Resolution Power

GPUs has been widely used in scientific computing, as by offering exceptional performance as by power-efficient hardware. Its position established in high-performance and scientific computing communities has increased the urgency of understanding the power cost of GPU usage in accurate measurements. For this, the use of internal sensors are extremely important. In this work, we employ the GPU sensors to obtain high-resolution power profiles of real and benchmark applications. We wrote our own tools to query the sensors of two NVIDIA GPUs from different generations and compare the accuracy of them. Also, we compare the power profile of GPU with CPU using IPMItool.

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