A High-Performance Parallel Computation Hardware Architecture in ASIC of SHA-256 Hash

2019 ◽  
Author(s):  
Xiaoyong Zhang ◽  
Ruizhen WU ◽  
Mingming Wang ◽  
Lin Wang
Keyword(s):  
Parallel Computation ◽  
High Performance ◽  
Hardware Architecture

Application of High Performance Parallel Computing Based on GPU

2013 ◽  
Vol 411-414 ◽  
pp. 585-588
Author(s):  
Liu Yang ◽  
Tie Ying Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Parallel Computing ◽  
Parallel Computation ◽  
High Performance ◽  
Search Process ◽  
Search Rate ◽  
Swarm Optimization ◽  
Path Search ◽  
Parallel Feature ◽  
Time And Space Complexity

This paper introduces parallel feature of the GPU, which will help GPU parallel computation methods to achieve the parallelization of PSO parallel path search process; and reduce the increasingly high problem of PSO (PSO: Particle Swarm Optimization) in time and space complexity. The experimental results show: comparing with CPU mode, GPU platform calculation improves the search rate and shortens the calculation time.

A high performance hardware architecture for multi-frame hierarchical motion estimation

2011 ◽  
Vol 57 (2) ◽  
pp. 794-801 ◽  
Author(s):  
Huong Ho ◽  
Robert Klepko ◽  
Nam Ninh ◽  
Demin Wang
Keyword(s):  
Motion Estimation ◽  
High Performance ◽  
Hardware Architecture

scholarly journals Data Centric Framework for Large-scale High-performance Parallel Computation

2014 ◽  
Vol 29 ◽  
pp. 2336-2350 ◽  
Author(s):  
Kenji Ono ◽  
Yasuhiro Kawashima ◽  
Tomohiro Kawanabe
Keyword(s):  
Parallel Computation ◽  
High Performance ◽  
Large Scale

AN EFFICIENT HARDWARE ARCHITECTURE TO SUPPORT COMPLEX FUZZY REASONING

1996 ◽  
Vol 05 (01n02) ◽  
pp. 41-60 ◽  
Author(s):  
G. ASCIA ◽  
V. CATANIA
Keyword(s):  
Computational Model ◽  
Parallel Computation ◽  
Membership Function ◽  
Fuzzy Reasoning ◽  
Hardware Architecture ◽  
Processor Performance

The paper presents the design of a VLSI fuzzy processor which is capable of supporting complex fuzzy reasoning. The architecture of the processor is based on a appropriate computational model, whose main features are: capability to cope with rule chaining; pre-processing of inferences to reduce the number of rules to be processed; parallel computation of the degree of activation of the rules; optimized representation of membership function. The processor performance is in the order of 1.5 MFLIPS (256 rule, 8 Fuzzy inputs, 4 output).

scholarly journals A Parallel Implementation of Unscheduled Flow Control in Interconnected Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-19
Author(s):  
G. Ozdemir Dag ◽  
Mustafa Bagriyanik
Keyword(s):  
Power Systems ◽  
Power System ◽  
Parallel Computation ◽  
High Performance ◽  
Power Flow ◽  
Parallel Implementation ◽  
Computation Time ◽  
Test System ◽  
Fuzzy Decision Making ◽  
Optimization Approach

The unscheduled power flow problem needs to be minimized or controlled as soon as possible in a deregulated power system since the transmission systems are mostly operated at their power-carrying limits or very close to it. The time spent for simulations to determine the current states of all the system and control variables of the interconnected power system is important. Taking necessary action in case of any failure of equipment or any other occurrence of an undesired situation could be critical. Using supercomputing facilities and parallel computing techniques together decreases the computation time greatly. In this study, a parallel implementation of a multiobjective optimization approach based on both genetic algorithms and fuzzy decision making to manage unscheduled flows is presented. Parallel computation techniques are applied using supercomputers (high-performance computers). The proposed method is applied to the IEEE 300 bus test system. Two different cases for some parameters of GA are considered to see the power of parallel computation technique. Then the simulation results are presented.

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