Tools to Compile Dataflow Programs for Manycores

The arrival of manycore systems enforces new approaches for developing applications in order to exploit the available hardware resources. Developing applications for manycores requires programmers to partition the application into subtasks, consider the dependence between the subtasks, understand the underlying hardware and select an appropriate programming model. This is complex, time-consuming and prone to error. In this thesis, we identify and implement abstraction layers in compilation tools to decrease the burden of the programmer, increase program portability and scalability, and increase retargetability of the compilation framework. We present compilation frameworks for two concurrent programming languages, occam-pi and CAL Actor Language, and demonstrate the applicability of the approach with application case-studies targeting these different manycore architectures: STHorm, Epiphany, Ambric, EIT, and ePUMA. For occam-pi, we have extended the Tock compiler and added a backend for STHorm. We evaluate the approach using a fault tolerance model for a four stage 1D-DCT algorithm implemented by using occam-pi's constructs for dynamic reconguration, and the FAST corner detection algorithm which demonstrates the suitability of occam-pi and the compilation framework for data-intensive applications. For CAL, we have developed a new compilation framework, namely Cal2Many. The Cal2Many framework has a front end, two intermediate representations and four backends: for a uniprocessor, Epiphany, Ambric, and a backend for SIMD based architectures. Also, we have identied and implemented of CAL actor fusion and fission methodologies for efficient mapping CAL applications. We have used QRD, FAST corner detection, 2D-IDCT, and MPEG applications to evaluate our compilation process and to analyze the limitations of the hardware