Purpose The purpose of this work is to present a methodology that harnesses the computational power of multiple graphics processing units (GPUs) and hides the complexities of tuning GPU parameters from the users. Design/methodology/approach A methodology for auto-tuning OpenCL configuration parameters has been developed. Findings This described process helps simplify coding and generates a significant gain in time for each method execution. Originality/value Most authors develop their GPU applications for specific hardware configurations. In this work, a solution is offered to make the developed code portable to any GPU hardware.

Parallel computing systems have become pervasive, being used to interact with the physical world and process a large amount of data from various sources. It is essential, therefore, the continuous improvement of computational performance to keep up with the increasing rate of the amount of information that needs to be processed. Some of these applications admit lower quality in the final result in exchange for increased execution performance. This work aims to evaluate the feasibility of using supervised learning methods to ensure that the Relaxed Synchronization technique, used to increase execution performance, provides results within acceptable limits of error. To do so, we have created a methodology that uses some input data to assemble test cases that, when executed, will provide input values for the training of supervised learning methods. This way, when the user uses his/her application (in the same training environment) with a new input, the trained classification algorithm will suggest the relax synchronization factor that is best suited to the triple application/input/execution environment. We used this methodology in some well-known parallel applications and showed that, by combining Relaxed Synchronization with supervised learning methods, it was possible to maintain the maximum established error rate. In addition, we evaluated the performance gain obtained with this technique for a number of scenarios in each application.