A high-performance and energy-efficient architecture for floating-point based LUdecomposition on FPGAs

Author(s):  
G. Govindu ◽  
Seonil Choi ◽  
V. Prasanna ◽  
V. Daga ◽  
S. Gangadharpalli ◽  
...  
Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 376
Author(s):  
Matej Špeťko ◽  
Ondřej Vysocký ◽  
Branislav Jansík ◽  
Lubomír Říha

Nvidia is a leading producer of GPUs for high-performance computing and artificial intelligence, bringing top performance and energy-efficiency. We present performance, power consumption, and thermal behavior analysis of the new Nvidia DGX-A100 server equipped with eight A100 Ampere microarchitecture GPUs. The results are compared against the previous generation of the server, Nvidia DGX-2, based on Tesla V100 GPUs. We developed a synthetic benchmark to measure the raw performance of floating-point computing units including Tensor Cores. Furthermore, thermal stability was investigated. In addition, Dynamic Frequency and Voltage Scaling (DVFS) analysis was performed to determine the best energy-efficient configuration of the GPUs executing workloads of various arithmetical intensities. Under the energy-optimal configuration the A100 GPU reaches efficiency of 51 GFLOPS/W for double-precision workload and 91 GFLOPS/W for tensor core double precision workload, which makes the A100 the most energy-efficient server accelerator for scientific simulations in the market.


2015 ◽  
Vol 1 (4) ◽  
pp. 1-12
Author(s):  
Chidadala Janardhan ◽  
◽  
Bhagath Pyda ◽  
J. Manohar ◽  
K. V. Ramanaiah ◽  
...  

Author(s):  
Jack Dongarra ◽  
Laura Grigori ◽  
Nicholas J. Higham

A number of features of today’s high-performance computers make it challenging to exploit these machines fully for computational science. These include increasing core counts but stagnant clock frequencies; the high cost of data movement; use of accelerators (GPUs, FPGAs, coprocessors), making architectures increasingly heterogeneous; and multi- ple precisions of floating-point arithmetic, including half-precision. Moreover, as well as maximizing speed and accuracy, minimizing energy consumption is an important criterion. New generations of algorithms are needed to tackle these challenges. We discuss some approaches that we can take to develop numerical algorithms for high-performance computational science, with a view to exploiting the next generation of supercomputers. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.


2019 ◽  
Vol 15 (4) ◽  
pp. 1-21
Author(s):  
Bing Li ◽  
Mengjie Mao ◽  
Xiaoxiao Liu ◽  
Tao Liu ◽  
Zihao Liu ◽  
...  

Nano Energy ◽  
2021 ◽  
Vol 82 ◽  
pp. 105717
Author(s):  
Min-Ci Wu ◽  
Jui-Yuan Chen ◽  
Yi-Hsin Ting ◽  
Chih-Yang Huang ◽  
Wen-Wei Wu

Author(s):  
Wei-Song Hung ◽  
Subrahmanya T M ◽  
Po Ting Lin ◽  
Yu-Hsuan Chiao ◽  
Januar Widakdo ◽  
...  

Membrane distillation (MD) based desalination process is thought to be a promising strategy to address global challenges such as safe water-energy crisis and environmental pollution. Here, we demonstrate a novel...


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