  Parallel computing can offer an enormous advantage regarding the performance
for very large applications in almost any field: scientific computing, computer
vision, databases, data mining, and economics. GPUs are high performance
many-core processors that can obtain very high FLOP rates. Since the first idea
of using GPU for general purpose computing, things have evolved and now there
are several approaches to GPU programming: CUDA from NVIDIA and Stream from
AMD. CUDA is now a popular programming model for general purpose computations
on GPU for C/C++ programmers. A great number of applications were ported to
CUDA programming model and they obtain speedups of orders of magnitude
comparing to optimized CPU implementations. In this paper we present an
implementation of a library for solving linear systems using the CCUDA
framework. We present the results of performance tests and show that using GPU
one can obtain speedups of about of approximately 80 times comparing with a CPU
implementation.
