Auto-tuning techniques for linear algebra routines on hybrid platforms

• We analyse techniques for auto-tuning linear algebra routines on hybrid systems.
• Experimental and model based methods are studied.
• Experiments are carried out in multicore CPU together with manycore coprocessors.
• The study is carried out with the matrix multiplication and LU factorization.

This work analyses two techniques for auto-tuning linear algebra routines for hybrid combinations of multicore CPU and manycore coprocessors (single or multiple GPUs and MIC). The first technique is based on basic models of the execution time of the routines, whereas the second one manages only empirical information obtained during the installation of the routines. The final goal in both cases is to obtain a balanced assignation of the work to the computing components in the system. The study is carried out with a basic kernel (matrix–matrix multiplication) and a higher level routine (LU factorization) which uses the auto-tuned basic routine. Satisfactory results are obtained, with experimental execution times close to the lowest experimentally achievable.