Solving dense linear systems with hybrid ARM+GPU platforms

The necessity of reducing the energy consumption while improving the computational performance has encouraged the development of new hardware platforms. In this line, hybrid architectures that integrate ARM processors with graphics accelerators offer a positive balance between computing capabilities and energy requirements. However, in order to make an efficient use of this hardware, it is necessary to develop new methods and computational kernels, as well as to adapt existing ones. The solution of linear systems of equations is a basic operation in the solution of different problems. Its relevance and computational cost has motivated an important amount of work, and in consequence, it is possible to find high performance solvers for most hardware platforms. In this work we study the solution of dense linear systems of equations in an NVIDIA Jetson TK1 device via the Gauss-Huard method. The experimental evaluation shows that the new solvers outperform the ones available in the MAGMA library for systems of dimesion n ≤ 6,000.