targetDP: an Abstraction of Lattice Based Parallelism with Portable Performance

Dr Alan Gray, EPCC, The University of Edinburgh

To achieve high performance on modern computers, it is vital to map algorithmic parallelism to that inherent in the hardware. From an application developer's perspective, it is also important that code can be maintained in a portable manner across a range of hardware. I will present targetDP (target Data Parallel), a lightweight programming layer that allows the abstraction of data parallelism for applications that employ structured grids. A single source code may be used to target both thread level parallelism (TLP) and instruction level parallelism (ILP) on either SIMD multi-core CPUs or GPU-accelerated platforms. targetDP is implemented via standard C preprocessor macros and library functions, can be added to existing applications incrementally, and can be combined with higher-level paradigms such as MPI. We present CPU and GPU performance results for a benchmark taken from Ludwig, the lattice Boltzmann application that motivated this work. These demonstrate not only performance portability, but also the optimisation resulting from the intelligent exposure of ILP. A new ARCHER eCSE project has recently started to fully port Ludwig to the targetDP model, to improve performance on ARCHER whilst allowing performance portability to accelerated architectures.