O'Boyle, Michael

Franke, Bjoern

Chandramohan, Kiran

Engineering and Physical Sciences Research Council (EPSRC)

2017-05-17T15:06:28Z

2017-05-17T15:06:28Z

2016-06-27

Most embedded devices are based on heterogeneous Multiprocessor System on Chips (MPSoCs). These contain a variety of processors like CPUs, micro-controllers, DSPs, GPUs and specialised accelerators. The heterogeneity of these systems helps in achieving good performance and energy efficiency but makes programming inherently difficult. There is no single programming language or runtime to program such platforms. This thesis makes three contributions to these problems. First, it presents a framework that allows code in Single Program Multiple Data (SPMD) form to be mapped to a heterogeneous platform. The mapping space is explored, and it is shown that the best mapping depends on the metric used. Next, a compiler framework is presented which bridges the gap between the high -level programming model of OpenMP and the heterogeneous resources of MPSoCs. It takes OpenMP programs and generates code which runs on all processors. It delivers programming ease while exploiting heterogeneous resources. Finally, a compiler-based approach to runtime power management for heterogeneous cores is presented. Given an externally provided budget, the approach generates heterogeneous, partitioned code that attempts to give the best performance within that budget.

http://hdl.handle.net/1842/22028

en

The University of Edinburgh

Kiran Chandramohan and Michael F. P. O’Boyle. A compiler framework for automatically mapping data parallel programs to heterogeneous mpsocs. In Proceedings of the 2014 International Conference on Compilers, Architecture and Synthesis for Embedded Systems, CASES ’14, pages 9:1–9:10, New York, NY, USA, 2014. ACM.

Kiran Chandramohan and Michael F.P. O’Boyle. Partitioning data-parallel programs for heterogeneous mpsocs: Time and energy design space exploration. In Proceedings of the 2014 SIGPLAN/SIGBED Conference on Languages, Compilers and Tools for Embedded Systems, LCTES ’14, pages 73–82, New York, NY, USA, 2014. ACM.

heterogeneous processors

compiler

Mapping parallelism to heterogeneous processors

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy