Abstract. With the increasing architectural diversity of many-core architectures the challenges of parallel programming and code portability will sharply rise. The EU project PEPPHER addresses these issues with a component-based approach to application development on top of a taskparallel execution model. Central to this approach are multi-architectural components which encapsulate different implementation variants of application functionality tailored for different core types. An intelligent runtime system selects and dynamically schedules component implementation variants for efficient parallel execution on heterogeneous many-core architectures. On top of this model we have developed language, compiler and runtime support for a specific class of applications that can be expressed using the pipeline pattern. We propose C/C++ language annotations for specifying pipeline patterns and describe the associated compilation and runtime infrastructure. Experimental results indicate that with our high-level approach performance comparable to manual parallelization can be achieved.

No abstract

The goal of the Demaq/TransScale system is to automate the distribution of complex application processes to large numbers of hosts. We implement distribution as a source-level transformation that turns the distribution-unaware application specification for a single host into a set of programs that can be executed on the various machines of a cluster.

The goal of the Demaq project is to investigate a novel way of thinking about distributed applications that are based on the asynchronous exchange of XML messages. Unlike today's solutions that rely on imperative programming languages and multi-tiered application servers, Demaq uses a declarative language for implementing the application logic as a set of rules. A rule compiler transforms the application specifications into execution plans against the message history. The plans are evaluated using our optimized runtime engine. This allows us to leverage existing knowledge about declarative query processing for optimizing distributed applications.