Polyhedral Compilation for Data Flow Computers

Abstract

The increasing transistor density of Integrated Circuits (ICs) ever since their introduction, has scaled the computational performance of microprocessors. As a consequence of the gain in transistor density, the power dissipation density has also increased to a degree that has become a limiting factor in further performance scaling. The prevalent control flow computing paradigm is, especially in the context of High Performance Computing (HPC), faced with this power crisis. Data flow computing can achieve a better computations per unit power ratio and might therefore be the solution to achieving exa-scale computing, enabling progress in science and technology.
This work contributes in investigating methods for increasing the abstraction
level of data flow programming by using a Polyhedral Process Network (PPN) as abstraction to facilitate automated analysis and transformation of data flow applications. A Polyhedral Process Network (PPN) is a process network based on Polyhedral Compilation (PC) a mathematical framework for code analysis and optimisations.
A tool has been implemented to translate a PPN to a data flow implementation. The Maxeler Data Flow Engine (DFE) technology, a data flow hardware platform supported by a tool-flow, is used as target technology. For a number of specific challenges involved in the transformation from PPN to DFE-implementation a method has been implemented. This has shown that a PPN as well as the capabilities of PC are useful concepts that are applicable to further increase of the abstraction-level of data flow computing. However the transformation from PPN to DFE-implementation is complex. Therefore this work concludes that the development of a data flow tailored Polyhedral Compilation (PC) abstraction is the most promising future direction.