Adaptive Parallel Interval Global Optimization Algorithms Based on their Performance for Non-dedicated Multicore Architectures

Branch and Bound (B&B) algorithms are highly parallelizable but they are irregular and dynamic load balancing techniques have been used to avoid idle processors. In previous work, authors use a dynamic number of threads at run time, which depends on the measured performance of the application for just one interval B&B algorithm running on the system. In this way, load balancing is achieved by thread generation decisions. In this work, we extend the study of these models to non-dedicated systems. In order to have a controlled test bed and comparable results, several instances of the interval global optimization algorithm are executed in the system, with the same model and problem to solve. Therefore, a non-dedicated system is simulated because the execution of one application affects the execution of the other instances. This paper discusses different methods and models to decide when a thread should be created. Experiments show which of the proposed methods performs best in terms of maximum running time per application, using the fewest running threads. Following this parallel programming methodology, which is well suited for other B&B codes, applications can adapt their parallelism level to their performance and load of the system(at run time). This work represents a step forward towards increasing the performance of parallel algorithm running in non-dedicated and heterogeneous systems. The adaptive model discussed in this work is able to reduce the overall execution time for a set of instances of the same application running simultaneously. It also exempts the user from specifying the number of threads each application should use.