Process malleability has proved to have a highly positive impact on the resource utilization and global productivity in data centers compared with the conventional static resource allocation policy. However, the non-negligible additional development effort this solution imposes has constrained its adoption by the scientific programming community. In this work, we present DMRlib, a library designed to offer the global advantages of process malleability while providing a minimalist MPI-like syntax. The library includes a series of predefined communication patterns that greatly ease the development of malleable applications. In addition, we deploy several scenarios to demonstrate the positive impact of process malleability featuring different scalability patterns. Concretely, we study two job submission modes (rigid and moldable) in order to identify the best-case scenarios for malleability using metrics such as resource allocation rate, completed jobs per second, and energy consumption. The experiments prove that our elastic approach may improve global throughput by a factor higher than 3x compared to the traditional workloads of non-malleable jobs. This work was supported by projects TIN2014-53495-R, TIN2015-65316-P, and TIN2017-82972-R from MINECO and FEDER. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement No. 749516. Sergio Iserte was supported by a postdoctoral fellowship from Generalitat Valenciana and European Social Fund APOSTD/2020/026. Finally, the authors want to thank the anonymous reviewers whose suggestions significantly improved the quality of this manuscript. Peer Reviewed