high performance computing on graphics processing units: hgpu.org

We present an implementation of Overdetermined Laplacian Partial Differentiation Equations (ODETLAP) that uses CUDA directly. This lossy compression technique approximates a solution to an overdetermined system of equations in order to reconstruct gridded, correlated data. ODETLAP can be used to compress a dataset or to reconstruct missing data. Parallelism in CUDA provides speed performance improvements over other implementation methods. ODETLAP is inspired by the Laplacian Partial Differential Equation, though it is capable of preserving local extrema. Typical compression techniques are limited to viewing data as being one dimensional. The ODETLAP algorithm instead utilizes the autocorrelation of data in multiple dimensions to perform compression. This allows for improved compression of higher dimensional datasets, as can be found in geographical and environmental data[1, 2]. ODETLAP requires the construction and solution approximation of a sparse over-determined linear system of equations. As a result, the algorithm is quite compute-intensive. Parallelization techniques offer means of improving performance of this algorithm. The CUDA libraries, Thrust and CUSP, will be utilized in this parallel implementation of ODETLAP The Thrust library provides means for construction of the overdetermined system matrix, while the CUSP library contains solvers for such systems.