Heterogeneous cluster for acceleration of linear algebra computations
Authors: Šimeček I., Buk Z.
GPGPU; grid computing; remote calls; library for numerical linear algebra.
This project deals with a new concept of the heterogeneous grid for acceleration of the numerical linear algebra computing.
We design this grid with respect to maximal ratio between cost and computational power.
It allows a parallelization of scientific codes with minimal programming effort.
We also optimize grid concept to be less sensitive to network parameters.
March 10, 2011 - version 0.1 of this project is released.
July 20, 2011 - version 0.2 of this project is released.
December 1, 2011 - version 1.0 of this project is released.
Only for demonstartion purposes !
For fully funcionality it requires with:
1) CUDA SDK with CUBLAS ( for GPGPU dense matrix computing),
2) MPI implementation
User's guide PDF file (in Czech)
Final report PDF file (in Czech)
1) CUSPARSE ( for GPGPU sparse matrix computing),
2) standard library for vector and matrix manipulation like ATLAS or GOTO,
3) sparse matrix solver (like SuperLU or UMFpack).
Used in papers:
APLIMAT 2012: Heterogeneous cluster for acceleration of linear algebra computations
ICPM 2011: Experimental grid for numerical linear algebra
SNA 2011: Experimental grid for numerical linear algebra