Meeting the 20MW power envelope sought for exascale is one of the greatest challenges in designing those class of systems. Addressing this challenge requires over-provisioned and dynamically reconfigurable system with fine-grained control on power and speed of the individual cores. In this paper, we present EfficientSpeed (ES), a library that improves energy efficiency in scientific computing by carefully selecting the speed of the processor. The run-time component of ES adjusts the speed of the processor (via DVFS and clock modulation) dynamically while preserving the desired level of the performance. These adjustments are based on online performance and energy measurements, user-selected policies that dictate the aggressiveness of adjustments, and user-defined performance requirements. Our results quantify the best energy savings that can be achieved by controlling the speed of the processor, with today's technology, at the cost of negligible performance degradation. We then demonstrate that ES is effective in automatically calibrating the speed of execution in real applications, saving energy and meeting the desired performance goal.We evaluate ES on GAMESS, an ab initio quantum chemistry package. We show that ES respects the stipulated 5% performance loss bound and achieves 16% decrease in energy required to complete the execution while running with a power draw that is 18% lower.