Reduced-Precision Floating-Point Formats on GPUs for High Performance and Energy Efficient Computation

2016 ◽  
Author(s):  
Daichi Mukunoki ◽  
Toshiyuki Imamura
Keyword(s):  
Energy Efficient ◽  
High Performance ◽  
Floating Point ◽  
Efficient Computation

scholarly journals High Performance Energy Efficient Computation Elements of Processing Unit

2019 ◽  
Vol 9 (2) ◽  
pp. 2450-2455
Keyword(s):  
Energy Efficient ◽  
High Performance ◽  
Logic Gate ◽  
Processing Unit ◽  
Efficient Computation ◽  
Quartus Ii ◽  
Efficient Processing ◽  
Exclusive Or ◽  
Design Logic

in our manuscript, various circuits for arithmetic summation are compared. Cadence 90nm technology and Quartus II EP2C20F484C7 are used for implementation of design. Logic gate-based adders, PFCA, TG and HSD technique-based adders characteristics are analyzed. Y finding is PFCA with 10T transistor performs slightly efficient compare to its counterpart. Exclusive OR-NOR design is optimum for least delay Adders for high performance energy efficient processing unit.

scholarly journals A Survey on Accelerated Mapreduce for Hadoop

2017 ◽  
Vol 10 (3) ◽  
pp. 597-602
Author(s):  
Jyotindra Tiwari ◽  
Dr. Mahesh Pawar ◽  
Dr. Anjajana Pandey
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Energy Efficient ◽  
High Performance ◽  
Map Reduce ◽  
Efficient Computation ◽  
Apache Hadoop ◽  
Huge Data ◽  
Performance Techniques ◽  
Big Data Storage

Big Data is defined by 3Vs which stands for variety, volume and velocity. The volume of data is very huge, data exists in variety of file types and data grows very rapidly. Big data storage and processing has always been a big issue. Big data has become even more challenging to handle these days. To handle big data high performance techniques have been introduced. Several frameworks like Apache Hadoop has been introduced to process big data. Apache Hadoop provides map/reduce to process big data. But this map/reduce can be further accelerated. In this paper a survey has been performed for map/reduce acceleration and energy efficient computation in quick time.

scholarly journals DGX-A100 Face to Face DGX-2—Performance, Power and Thermal Behavior Evaluation

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 376
Author(s):  
Matej Špeťko ◽  
Ondřej Vysocký ◽  
Branislav Jansík ◽  
Lubomír Říha
Keyword(s):  
Artificial Intelligence ◽  
Thermal Behavior ◽  
Energy Efficient ◽  
High Performance ◽  
Floating Point ◽  
Double Precision ◽  
Face To Face ◽  
Scientific Simulations ◽  
Performance Computing ◽  
Dynamic Frequency

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.

Challenges And Energy Efficient Techniques For High Performance Wireless Communications

2015 ◽  
Vol 1 (4) ◽  
pp. 1-12
Author(s):  
Chidadala Janardhan ◽  
◽  
Bhagath Pyda ◽  
J. Manohar ◽  
K. V. Ramanaiah ◽  
...  
Keyword(s):  
Wireless Communications ◽  
Energy Efficient ◽  
High Performance

scholarly journals Numerical algorithms for high-performance computational science

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190066 ◽  
Author(s):  
Jack Dongarra ◽  
Laura Grigori ◽  
Nicholas J. Higham
Keyword(s):  
High Performance ◽  
Numerical Algorithms ◽  
Computational Science ◽  
Floating Point ◽  
Important Criterion ◽  
Data Movement ◽  
Floating Point Arithmetic ◽  
High Performance Computers ◽  
Point Arithmetic ◽  
Speed And Accuracy

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’.

scholarly journals Thread Batching for High-performance Energy-efficient GPU Memory Design

2019 ◽  
Vol 15 (4) ◽  
pp. 1-21
Author(s):  
Bing Li ◽  
Mengjie Mao ◽  
Xiaoxiao Liu ◽  
Tao Liu ◽  
Zihao Liu ◽  
...  
Keyword(s):  
Energy Efficient ◽  
High Performance ◽  
Memory Design

A novel high-performance and energy-efficient RRAM device with multi-functional conducting nanofilaments

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