A novel multi-graphics processing unit parallel optimization framework for the sparse matrix-vector multiplication

2016 ◽  
Vol 29 (5) ◽  
pp. e3936 ◽  
Author(s):  
Jiaquan Gao ◽  
Yu Wang ◽  
Jun Wang
Keyword(s):  
Graphics Processing Unit ◽  
Sparse Matrix ◽  
Parallel Optimization ◽  
Processing Unit ◽  
Optimization Framework ◽  
Matrix Vector Multiplication ◽  
Graphics Processing ◽  
Matrix Vector

GPU Accelerated Reconstruction in Compton Scattering Tomography Using Matrix Compression

2014 ◽  
Vol 519-520 ◽  
pp. 102-107
Author(s):  
Yu Fei Yu ◽  
Bin Yan ◽  
Biao Wang ◽  
Lei Li ◽  
Yu Han ◽  
...  
Keyword(s):  
Compton Scattering ◽  
Graphics Processing Unit ◽  
Sparse Matrix ◽  
Reconstruction Algorithm ◽  
Processing Unit ◽  
Matrix Vector Multiplication ◽  
Speedup Ratio ◽  
Parallel Features ◽  
Graphics Processing ◽  
Matrix Vector

An acceleration strategy for TV-ADM reconstruction algorithm in Compton scattering tomography (CST) is proposed. By analyzing the sparse characteristic of CST projection matrixes, firstly, the sparse matrix vector CSR format and ELL format are used to store them, which greatly reduce the memory consumption. Then, a Sparse Matrix Vector multiplication (SpMV) method is utilized to accelerate the projector and back projector process. Finally, based on the parallel features, the TV-ADM is computed with Graphics Processing Unit (GPU). Numerical experiments show that the TV-ADM with the presented acceleration strategy could achieve a 96 times speedup ratio and 224 times memory compression ratio without precision loss.

Parallel computations of the step response of a floor heater with the use of a graphics processing unit. Part 2: results and their evaluation

2013 ◽  
Vol 61 (4) ◽  
pp. 949-954 ◽  
Author(s):  
J. Gołębiowski ◽  
J. Forenc
Keyword(s):  
Graphics Processing Unit ◽  
Sparse Matrix ◽  
Temporal Distribution ◽  
Step Response ◽  
Processing Unit ◽  
Commercial Program ◽  
Speed Up ◽  
Spatio Temporal ◽  
Graphics Processing ◽  
Linear Systems Of Equations

Abstract Using models and algorithms presented in the first part of the article, a spatio-temporal distribution of the step response of a floor heater was determined. The results have been presented in the form of heating curves and temperature profiles of the heater in the selected time moments. The computations results were verified through comparing them with the solution obtained with the use of a commercial program - NISA. Additionally, the distribution of the average time constant of thermal processes occurring in the heater was determined. The analysis of the use of a graphics processing unit in numerical computations based on the conjugate gradient method was done. It was proved that the use of a graphics processing unit is profitable in the case of solving linear systems of equations with dense coefficient matrices. In the case of a sparse matrix, the speed-up depends on the number of its non-zero elements.

A Novel Multi-GPU Parallel Optimization Model for The Sparse Matrix-Vector Multiplication

2016 ◽  
Vol 26 (04) ◽  
pp. 1640001
Author(s):  
Jiaquan Gao ◽  
Yuanshen Zhou ◽  
Kesong Wu
Keyword(s):  
Optimization Model ◽  
Graphics Processing Units ◽  
High Efficiency ◽  
Sparse Matrix ◽  
Performance Model ◽  
Parallel Optimization ◽  
Multiple Gpus ◽  
Matrix Vector Multiplication ◽  
Storage Format ◽  
Matrix Vector

Accelerating the sparse matrix-vector multiplication (SpMV) on the graphics processing units (GPUs) has attracted considerable attention recently. We observe that on a specific multiple-GPU platform, the SpMV performance can usually be greatly improved when a matrix is partitioned into several blocks according to a predetermined rule and each block is assigned to a GPU with an appropriate storage format. This motivates us to propose a novel multi-GPU parallel SpMV optimization model. Our model involves two stages. In the first stage, a simple rule is defined to divide any given matrix among multiple GPUs, and then a performance model, which is independent of the problems and dependent on the resources of devices, is proposed to accurately predict the execution time of SpMV kernels. Using these models, we construct in the second stage an optimally multi-GPU parallel SpMV algorithm that is automatically and rapidly generated for the platform for any problem. Given that our model for SpMV is general, independent of the problems, and dependent on the resources of devices, this model is constructed only once for each type of GPU. The experiments validate the high efficiency of our proposed model.

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