Efficiently exploiting thread-level parallelism has been challenging. Many parallel applications are not sufficiently balanced or CPU-bound to take advantage of the increasing number of cores and the highest possible operating frequency. Moreover, many variables may change according to the system (input set, microarchitecture, and number of cores) or during execution, influencing each parallel region in different ways. Therefore, the task of rightly choosing the ideal configuration (number of threads and DVFS) for each parallel region to deliver the best Energy-Delay Product (EDP) is not straightforward. While the significant number of variables prevents the use of exhaustive search methods, the changing nature of the problem precludes offline strategies. Few solutions are online and synergistically consider thread throttling and DVFS. However, they lack transparency (demand changes in the original code) and/or adaptability (do not automatically adjust to applications at run-time). Our proposed Hoder covers all the characteristics above, optimizing at run-time any dynamically linked OpenMP application, without requiring any code transformation or recompilation. We show Hoder's efficiency by comparing it to two exhaustive offline and two online search approaches, three state-of-the-art techniques, and regular OpenMP execution, considering different setups (Intel 44-, 16- and 12-core; AMD 8- and 12-core).

Efficiently exploiting thread-level parallelism has been challenging for software developers. As many parallel applications do not scale with the number of cores, the task of rightly choosing the ideal amount of threads to produce the best results in performance or energy is not straightforward. Moreover, many variables may change according to the system at hand (e.g., application, input set, microarchitecture, number of cores) and even during execution. Existing solutions lack transparency (demand changes in the original code) or adaptability (do not automatically adjust to applications at run-time). In this scenario, we propose Aurora, an OpenMP framework that is completely transparent to both the designer and end-user. Without any code transformation or recompilation, it is capable of automatically finding, at run-time and with minimum overhead, the optimal number of threads for each parallel loop region and re-adapt in cases the behavior of a region changes during execution. When executing fifteen well-known benchmarks on four multi-core processors, Aurora improves the Energy-Delay Product by up to 98, 86 and 91 percent over the standard OpenMP execution, the OpenMP feature that dynamically adjusts the number of threads, and the Feedback-Driven Threading, respectively.