
Hierarchical Jacobi Iteration for Structured Matrices on GPUs using Shared Memory
High fidelity scientific simulations modeling physical phenomena typical...
read it

Multichannel Analysis of Surface Waves Accelerated (MASWAccelerated): Software for Efficient Surface Wave Inversion Using MPI and GPUs
Multichannel Analysis of Surface Waves (MASW) is a technique frequently ...
read it

A Scalable SharedMemory Parallel Simplex for LargeScale Linear Programming
We present a sharedmemory parallel implementation of the Simplex tablea...
read it

Accelerating Concurrent Heap on GPUs
Priority queue, often implemented as a heap, is an abstract data type th...
read it

Accelerating Energy Games Solvers on Modern Architectures
Quantitative games, where quantitative objectives are defined on weighte...
read it

High Performance Algorithms for Counting Collisions and Pairwise Interactions
The problem of counting collisions or interactions is common in areas as...
read it

Experimenting with Constraint Programming on GPU
The focus of my PhD thesis is on exploring parallel approaches to effici...
read it
Effective Implementation of GPUbased Revised Simplex algorithm applying new memory management and cycle avoidance strategies
Graphics Processing Units (GPUs) with high computational capabilities used as modern parallel platforms to deal with complex computational problems. We use this platform to solve largescale linear programing problems by revised simplex algorithm. To implement this algorithm, we propose some new memory management strategies. In addition, to avoid cycling because of degeneracy conditions, we use a tabu rule for entering variable selection in the revised simplex algorithm. To evaluate this algorithm, we consider two sets of benchmark problems and compare the speedup factors for these problems. The comparisons demonstrate that the proposed method is highly effective and solve the problems with the maximum speedup factors 165.2 and 65.46 with respect to the sequential version and Matlab Linprog solver respectively.
READ FULL TEXT
Comments
There are no comments yet.