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)
Programming manycore GPUs or multicore CPUs for high performance requires a careful balance of several hardware specific related factors, which is typically achieved by expert users through trial and error. To reduce the amount of handmade optimization time required to achieve optimal performance, general guidelines can be followed or different metrics can(More)