scholarly journals Enabling transparent hardware acceleration on Zynq SoC for scientific computing

2020 ◽  
Vol 17 (1) ◽  
pp. 30-35
Author(s):  
Luca Stornaiuolo ◽  
Filippo Carloni ◽  
Riccardo Pressiani ◽  
Giuseppe Natale ◽  
Marco Santambrogio ◽  
...  
2022 ◽  
Vol 15 (2) ◽  
pp. 1-35
Author(s):  
Tom Hogervorst ◽  
Răzvan Nane ◽  
Giacomo Marchiori ◽  
Tong Dong Qiu ◽  
Markus Blatt ◽  
...  

Scientific computing is at the core of many High-Performance Computing applications, including computational flow dynamics. Because of the utmost importance to simulate increasingly larger computational models, hardware acceleration is receiving increased attention due to its potential to maximize the performance of scientific computing. Field-Programmable Gate Arrays could accelerate scientific computing because of the possibility to fully customize the memory hierarchy important in irregular applications such as iterative linear solvers. In this article, we study the potential of using Field-Programmable Gate Arrays in High-Performance Computing because of the rapid advances in reconfigurable hardware, such as the increase in on-chip memory size, increasing number of logic cells, and the integration of High-Bandwidth Memories on board. To perform this study, we propose a novel Sparse Matrix-Vector multiplication unit and an ILU0 preconditioner tightly integrated with a BiCGStab solver kernel. We integrate the developed preconditioned iterative solver in Flow from the Open Porous Media project, a state-of-the-art open source reservoir simulator. Finally, we perform a thorough evaluation of the FPGA solver kernel in both stand-alone mode and integrated in the reservoir simulator, using the NORNE field, a real-world case reservoir model using a grid with more than 10 5 cells and using three unknowns per cell.


2013 ◽  
Vol 133 (2) ◽  
pp. 132-138
Author(s):  
Shuhei Isa ◽  
Chikatoshi Yamada ◽  
Yasunori Nagata

Author(s):  
Jiyang Yu ◽  
Dan Huang ◽  
Siyang Zhao ◽  
Nan Pei ◽  
Huixia Cheng ◽  
...  

Author(s):  
Hui Yang ◽  
Anand Nayyar

: In the fast development of information, the information data is increasing in geometric multiples, and the speed of information transmission and storage space are required to be higher. In order to reduce the use of storage space and further improve the transmission efficiency of data, data need to be compressed. processing. In the process of data compression, it is very important to ensure the lossless nature of data, and lossless data compression algorithms appear. The gradual optimization design of the algorithm can often achieve the energy-saving optimization of data compression. Similarly, The effect of energy saving can also be obtained by improving the hardware structure of node. In this paper, a new structure is designed for sensor node, which adopts hardware acceleration, and the data compression module is separated from the node microprocessor.On the basis of the ASIC design of the algorithm, by introducing hardware acceleration, the energy consumption of the compressed data was successfully reduced, and the proportion of energy consumption and compression time saved by the general-purpose processor was as high as 98.4 % and 95.8 %, respectively. It greatly reduces the compression time and energy consumption.


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