A fast parallel matrix inversion algorithm based on heterogeneous multicore architectures

2015 ◽  
Author(s):  
Denggao Yu ◽  
Shiwen He ◽  
Yongming Huang ◽  
Guangshi Yu ◽  
Luxi Yang
Keyword(s):  
Matrix Inversion ◽  
Multicore Architectures ◽  
Inversion Algorithm ◽  
Heterogeneous Multicore

scholarly journals Power-Aware Job Scheduling on Heterogeneous Multicore Architectures

2015 ◽  
Vol 26 (3) ◽  
pp. 868-877 ◽  
Author(s):  
Matteo Chiesi ◽  
Luca Vanzolini ◽  
Claudio Mucci ◽  
Eleonora Franchi Scarselli ◽  
Roberto Guerrieri
Keyword(s):  
Job Scheduling ◽  
Multicore Architectures ◽  
Heterogeneous Multicore

JParEnt: Parallel entropy decoding for JPEG decompression on heterogeneous multicore architectures

2017 ◽  
Vol 29 (15) ◽  
pp. e4111 ◽  
Author(s):  
Wasuwee Sodsong ◽  
Minyoung Jung ◽  
Jinwoo Park ◽  
Bernd Burgstaller
Keyword(s):  
Multicore Architectures ◽  
Heterogeneous Multicore

Exploiting Kant and Kimura’s Matrix Inversion Algorithm on FPGA

2017 ◽  
Author(s):  
Andre Bannwart Perina ◽  
Paulo Matias ◽  
Eduardo Marques ◽  
Vanderlei Bonato ◽  
Joao Miguel Gago Pontes de Brito Lima
Keyword(s):  
Matrix Inversion ◽  
Inversion Algorithm

scholarly journals Heterogeneous Multicore Parallel Programming for Graphics Processing Units

2009 ◽  
Vol 17 (4) ◽  
pp. 325-336 ◽  
Author(s):  
Francois Bodin ◽  
Stephane Bihan
Keyword(s):  
Parallel Programming ◽  
Graphics Processing Units ◽  
Peak Performance ◽  
Hardware Accelerators ◽  
Multicore Architectures ◽  
Computing Power ◽  
Heterogeneous Multicore ◽  
Product Group ◽  
Legacy Code ◽  
Graphics Processing

Hybrid parallel multicore architectures based on graphics processing units (GPUs) can provide tremendous computing power. Current NVIDIA and AMD Graphics Product Group hardware display a peak performance of hundreds of gigaflops. However, exploiting GPUs from existing applications is a difficult task that requires non-portable rewriting of the code. In this paper, we present HMPP, a Heterogeneous Multicore Parallel Programming workbench with compilers, developed by CAPS entreprise, that allows the integration of heterogeneous hardware accelerators in a unintrusive manner while preserving the legacy code.

scholarly journals Modified Conjugate Gradient Algorithms for Gram Matrix Inversion of Massive MIMO Downlink Linear Precoding

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2834-2840
Keyword(s):  
Conjugate Gradient ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Low Complexity ◽  
Matrix Inversion ◽  
Gram Matrix ◽  
Linear Precoding ◽  
Inversion Algorithm ◽  
The Matrix

This paper deals with various low complexity algorithms for higher order matrix inversion involved in massive MIMO system precoder design. The performance of massive MIMO systems is optimized by the process of precoding which is divided into linear and nonlinear. Nonlinear precoding techniques are most complex precoding techniques irrespective of its performance. Hence, linear precoding is generally preferred in which the complexity is mainly contributed by matrix inversion algorithm. To solve this issue, Krylov subspace algorithm such as Conjugate Gradient (CG) was considered to be the best choice of replacement for exact matrix inversions. But CG enforces a condition that the matrix needs to be Symmetric Positive Definite (SPD). If the matrix to be inverted is asymmetric then CG fails to converge. Hence in this paper, a novel approach for the low complexity inversion of asymmetric matrices is proposed by applying two different versions of CG algorithms- Conjugate Gradient Squared (CGS) and Bi-conjugate Gradient (Bi-CG). The convergence behavior and BER performance of these two algorithms are compared with the existing CG algorithm. The results show that these two algorithms outperform CG in terms of convergence speed and relative residue.

Sign in / Sign up

Export Citation Format

Share Document