Since the advent of electronic computing, the processors' clock speed has risen tremendously. Now that energy efficiency requirements have stopped that trend, the number of processing cores per machine started to rise. In near future, these cores will become more specialized, and their inter-connections will form complex networks, both on-chip and beyond. This trend opens new fields of applications for high performance computing: Heterogeneous architectures offer different functionalities and thus support a wider range of applications. The increased compute power of these systems allows more complex simulations and numerical computations. Falling costs enable even small companies to invest in multi-core systems and clusters. However, the growing complexity might impede this growth. Imagine a cluster of thousands of interconnected heterogeneous processor cores. A software developer will need a deep knowledge about the underlying infrastructure as well as the data and communication dependencies in her application to partition it optimally across the available cores. Moreover, a predetermined partitioning scheme cannot reflect failing processors or additionally provided resources. In our poster, we introduce J-Cell, a project that aims at simplifying high performance distributed computing. J-Cell offers a single system image, which allows applications to run transparently on heterogeneous multi-core machines. It distributes code, objects and threads onto the compute resources which may be added or removed at run-time. This dynamic property leads to an ad-hoc network of processors and cores. In this network, a fully decentralized object localization and retrieval algorithm guarantees the access to distributed shared objects.