Abstract
In this paper we present research on applying a domain specific high-level abstractions (HLA) development strategy with the aim to “future-proof” a key class of high performance computing (HPC) applications that simulate hydrodynamics computations at AWE plc. We build on an existing high-level abstraction framework, OPS, that is being developed for the solution of multi-block structured mesh-based applications at the University of Oxford. OPS uses an “active library” approach where a single application code written using the OPS API can be transformed into different highly optimized parallel implementations which can then be linked against the appropriate parallel library enabling execution on different back-end hardware platforms. The target application in this work is the CloverLeaf mini-app from Sandia National Laboratory’s Mantevo suite of codes that consists of algorithms of interest from hydrodynamics workloads. Specifically, we present (1) the lessons learnt in re-engineering an industrial representative hydro-dynamics application to utilize the OPS high-level framework and subsequent code generation to obtain a range of parallel implementations, and (2) the performance of the auto-generated OPS versions of CloverLeaf compared to that of the performance of the hand-coded original CloverLeaf implementations on a range of platforms. Benchmarked systems include Intel multi-core CPUs and NVIDIA GPUs, the Archer (Cray XC30) CPU cluster and the Titan (Cray XK7) GPU cluster with different parallelizations (OpenMP, OpenACC, CUDA, OpenCL and MPI). Our results show that the development of parallel HPC applications using a high-level framework such as OPS is no more time consuming nor difficult than writing a one-off parallel program targeting only a single parallel implementation. However the OPS strategy pays off with a highly maintainable single application source, through which multiple parallelizations can be realized, without compromising performance portability on a range of parallel systems.
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Notes
- 1.
A similar approach is used in the C kernel implementations of the original CloverLeaf application.
- 2.
On Intel compilers, IEEE_FLAGS=-ipo -fp-model strict -fp-model source -prec-div -prec-sqrt.
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Acknowledgements
This research is funded by the UK AWE plc. under project “High-level Abstractions for Performance, Portability and Continuity of Scientific Software on Future Computing Systems”.
The OPS project is funded by the UK Engineering and Physical Sciences Research Council projects EP/K038494/1,EP/K038486/1, EP/K038451/1 and EP/K038567/1 on “Future-proof massively-parallel execution of multi-block applications”and EP/J010553/1 “Software for Emerging Architectures” (ASEArch) project. This paper used the Archer UK National Supercomputing Service from time allocated through UK Engineering and Physical Sciences Research Council projects EP/I006079/1, EP/I00677X/1 on “Multi-layered Abstractions for PDEs”.
This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.
Cloverleaf development is supported by the UK Atomic Weapons Establishment under grants CDK0660 (The Production of Predictive Models for Future Computing Requirements) and CDK0724 (AWE Technical Outreach Programe) and also the Royal Society through their Industry Fellowship Scheme (IF090020/AM).
We are thankful to Endre László at PPKE Hungary for his contributions to OPS, David Beckingsale at the University of Warwick and Michael Boulton at the University of Bristol for their insights into the original CloverLeaf application and its implementation.
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Mudalige, G.R., Reguly, I.Z., Giles, M.B., Mallinson, A.C., Gaudin, W.P., Herdman, J.A. (2015). Performance Analysis of a High-Level Abstractions-Based Hydrocode on Future Computing Systems. In: Jarvis, S., Wright, S., Hammond, S. (eds) High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation. PMBS 2014. Lecture Notes in Computer Science(), vol 8966. Springer, Cham. https://doi.org/10.1007/978-3-319-17248-4_5
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