In recent years multiprocessor architectures have become mainstream, and multi-core processors are found in products ranging from small portable cell phones to large computer servers. In parallel, research on real-time systems has mainly focused on traditional single-core processors. Hence, in order for real-time systems to fully leverage on the extra capacity offered by new multi-core processors, new design techniques, scheduling approaches, and real-time analysis methods have to be developed.

In the multi-core and multiprocessor domain there are mainly two scheduling approaches, global and partitioned scheduling. Under global scheduling each task can execute on any processor at any time while under partitioned scheduling tasks are statically allocated to processors and migration of tasks among processors is not allowed. Besides simplicity and efficiency of partitioned scheduling protocols, existing scheduling and synchronization methods developed for single-core processor platforms can more easily be extended to partitioned scheduling. This also simplifies migration of existing systems to multi-cores. An important issue related to partitioned scheduling is distribution of tasks among processors which is a bin-packing problem.

In this thesis we propose a partitioning framework for distributing tasks on the processors of multi-core platforms. Depending on the type of performance we desire to achieve, the framework may distribute a task set differently, e.g., in an application in which tasks process huge amounts of data the goal of the framework may be to decrease cache misses.Furthermore, we propose a blocking-aware partitioning heuristic algorithm to distribute tasks onto the processors of a multi-core architecture. The objective of the proposed algorithm is to decrease blocking overhead of tasks which reduces the total utilization and has the potential to reduce the number of required processors.Finally, we have implemented a tool to facilitate evaluation and comparison of different multiprocessor scheduling and synchronization approaches, as well as different partitioning heuristics. We have applied the tool in the evaluation of several partitioning heuristic algorithms, and the tool is flexible to which any new scheduling or synchronization protocol as well as any new partitioning heuristic can easily be added.