Javier Cuenca

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In this paper the possibility of including automatic optimization techniques in the design of parallel dynamic programming algorithms in heterogeneous systems is analyzed. The main idea is to automatically approach the optimum values of a number of algorithmic parameters (number of processes, number of processors, processes per processor), and thus obtain(More)
In this work an architecture of an automatically tuned linear algebra library proposed in previous works is extended in order to adapt it to platforms where both the CPU load and the network traffic vary. During the installation process in a system, the linear algebra routines will be tuned automatically to the system conditions: hardware characteristics(More)
This paper presents a self-optimization methodology for parallel linear algebra routines on heterogeneous systems. For each routine, a series of decisions is taken automatically in order to obtain an execution time close to the optimum (without rewriting the routine's code). Some of these decisions are: the number of processes to generate, the heterogeneous(More)
The performance of parallel linear algebra routines can be improved automatically using different methods. Our technique is based on the modellisation of the execution time of each routine , using information generated by routines from lower levels. However, sometimes the information generated at one level is not accurate enough to be used satisfactorily at(More)
Introduction Multicore processor, cc-NUMA systems can offer performance improvements Necessary software optimization techniques to benefit from the potential of the hardware Modelling the execution time of the routine Apply some empirical approach to study the behavior In this work: Analysis of the behavior of multithread LAPACK routines on PLASMA and Intel(More)