Fine-grained parallelism framework with predictable work-stealing for real-time multiprocessor systems

Lately, parallel task models have received much attention in the development of real-time multiprocessor systems, as they allow highly compute-intensive tasks to have shorter deadlines which is very much required in modern reactive systems. However, missing modularity and portability can make parallel programming a cumbersome endeavor. As a consequence, compute-intensive sectors in the desktop and server segment have relied on parallelism frameworks such as Intel Threading Building Blocks, Cilk and OpenMP. These parallelism frameworks, however, are optimized for decent average case performance and consequently, do not meet the strict requirements imposed by real-time systems. In this paper, we present a proof-of-concept parallelism framework which was implemented in particular for soft real-time systems and having tight timing and safety requirements of such critical systems in mind. The proposed runtime system implements static memory allocation in a work-stealing environment that conforms to the strict space and tight probabilistic time bounds of work-stealing schedulers. Furthermore, we evaluate the performance of this framework by conducting multiprogrammed benchmarks on a real-time embedded multicore architecture.