a shared memory parallel algorithm for data reduction using the singular value decomposition
This paper presents a parallel version of a singular value decomposition (SVD) based data reduction method for remotely sensed data that uses a training data set to find good basis vectors for the reduced dimension data. The parallel algorithm is implemented using Fortran 95, OpenMP, and LAPACK, and speedup results are given for up to 128 1.6 GHz processors of an SGI Altix 3700 with 512 MB of RAM. Performance is evaluated for various parallel options including scheduling strategies, data initialization, use of dplace, variable storage options, and cache optimization. Parallel speedup (without including time for input/output) using 128 processors reaches 75 with static scheduling and 190 with dynamic (guided) scheduling.
shared memory singular value decomposition svd remote sensing feature reduction