Applications augmented with adaptive capabilities are becoming common in parallel computing environments. For large-scale scientific applications, dynamic adjustments to a computationally-intensive part may lead to a large pay-off in facilitating efficient execution of the entire application while aiming at avoiding resource contention. Application-specific knowledge, often best revealed during the run-time, is required to initiate and time these adjustments. In particular, General Atomic and Molecular Electronic Structure System (GAMESS) is a program for ab initio quantum chemistry that places significant demands on the high-performance computing platforms. Certain electronic structure calculations are characterized by high consumption of a particular resource, such as CPU, main memory, or disk I/O. This may lead to resource contention among concurrent GAMESS jobs and other programs in the dynamically changing environment. Thus, it is desirable to improve GAMESS calculations by means of dynamic adaptations. In this thesis, we show how an application- or algorithm-specific knowledge may play a significant role in achieving this goal. The choice of implementation is facilitated by a module-driven middleware easily integrated with GAMESS that assesses resource consumption and invokes GAMESS adaptations to the system environment. We show that the throughput of GAMESS jobs may be improved greatly as a result of such adaptations.