scholarly journals Dataflow in MATLAB: Algorithm Acceleration Through Concurrency

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 2308-2318 ◽  
Author(s):  
Travis F. Collins ◽  
Alexander M. Wyglinski
Keyword(s):  
Algorithm Acceleration

IA Algorithm Acceleration Using GPUs

2011 ◽  
pp. 873-878 ◽  
Author(s):  
Antonio Seoane ◽  
Alberto Jaspe
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Graphics Processing Units ◽  
High Performance ◽  
General Purpose ◽  
Algorithm Acceleration ◽  
Database Operations ◽  
Graphics Processing ◽  
Programmable Processors

Graphics Processing Units (GPUs) have been evolving very fast, turning into high performance programmable processors. Though GPUs have been designed to compute graphics algorithms, their power and flexibility makes them a very attractive platform for generalpurpose computing. In the last years they have been used to accelerate calculations in physics, computer vision, artificial intelligence, database operations, etc. (Owens, 2007). In this paper an approach to general purpose computing with GPUs is made, followed by a description of artificial intelligence algorithms based on Artificial Neural Networks (ANN) and Evolutionary Computation (EC) accelerated using GPU.

scholarly journals An algorithm acceleration framework for correlation-based feature selection

2021 ◽  
Vol 336 ◽  
pp. 07011
Author(s):  
Xuefeng Yan ◽  
Yuqing Zhang ◽  
Arif Ali Khan
Keyword(s):  
Feature Selection ◽  
Computational Efficiency ◽  
Repetition Rate ◽  
Classification Accuracy ◽  
Sharp Increase ◽  
Experimental Results ◽  
Algorithm Acceleration ◽  
Correlation Based Feature Selection ◽  
The Cost

Repeated calculations lead to a sharp increase in the time of correlation-based feature selection. Incremental iteration has been applied in some algorithms to improve the efficiency. However, the computational efficiency of correlation has generally be ignored. An algorithm acceleration framework for correlation-based feature selection (AFCFS) is proposed. In AFCFS, the criterion of the feature selection will be analyzed and reconstructed based on entropy granularity, and the algorithm structure will also be adjusted accordingly. Specifically, all repeated part of calculation will be saved in mapping tables and can be accessed in next time directly, so as to further reduce the calculation repetition rate and improve the efficiency. The experimental results show that AFCFS can greatly reduce the cost time of these algorithms, and keep the corresponding classification accuracy basically unchanged.

Algorithm Acceleration of Compressed Sensing with Cloud

2014 ◽  
Vol 13 (2) ◽  
pp. 269-277
Author(s):  
Zhang Yong-Ping ◽  
Zhang Gong-Xuan ◽  
Zhu Zhao-Meng
Keyword(s):  
Compressed Sensing ◽  
Algorithm Acceleration

Shooting-Bouncing-Rays Technique to Model Mine Tunnels: Algorithm Acceleration

2021 ◽  
Vol 35 (11) ◽  
pp. 1330-1331
Author(s):  
Stephen Kasdorf ◽  
Blake Troksa ◽  
Jake Harmon ◽  
Cam Key ◽  
Branislav Notaros
Keyword(s):  
Wireless Communication ◽  
Ray Tracing ◽  
Application Programming Interface ◽  
Underground Mine ◽  
Electromagnetic Analysis ◽  
Large Structures ◽  
Algorithm Acceleration ◽  
Application Programming ◽  
Programming Interface ◽  
Mine Tunnels

We present and discuss acceleration of a shooting and bouncing rays (SBR) algorithm for ray-tracing electromagnetic analysis of electrically very large structures such as underground mine tunnels at modern wireless communication frequencies. The acceleration is based on the parallelization of the SBR technique on NVIDIA GPUs using the OptiX application programming interface. The results show dramatic speedups of the parallel SBR algorithm compared with serial implementation.

scholarly journals Method for run time hardware code profiling for algorithm acceleration

2009 ◽  
Author(s):  
Vladimir Matev ◽  
Eduardo de la Torre ◽  
Teresa Riesgo
Keyword(s):  
Algorithm Acceleration ◽  
Run Time
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