The problem sizes that the community is dealing with today in both scientific re- search and day-to-day use computing exceed the capacity of modern shared-memory systems. With the increasing prevalence of powerful multi-core/heterogenous pro-cessors on portable devices and cloud computing clusters, the demand for portable mainstream programming models supporting scalable, portable and extensible distributed computing is also rapidly growing. In this dissertation, we present the distributed selector model enabled distributed programming runtime library: cluster-based Habanero Java Distributed Selector and the mobile platform based Distributed Actor Model for Mobile Platforms by extending the HJDS implementation. This work focuses on enabling distributed message passing through building the communication middleware for an actor/selector model by supporting a fully actor-based runtime communication layer on clusters and a highly decoupled and customizable communication middleware and publish-subscribe enabled application-level runtime event handling on mobile devices that address the need for an easy-to-use, portable, reusable and scalable framework for small to medium sized distributed applications. We demonstrated the scalability of computationally intensive applications using distributed cluster-based and mobile-based platforms, and discuss the future steps for expanding the HJDS and DAMMP framework.