Abstract / Description of output
Remote Memory Access (RMA), also known as single‐sided communications, provides a way for reading and writing directly into the memory of other processes without having to issue explicit message passing style communication calls. Previous studies have concluded that MPI RMA can provide increased communication performance over traditional MPI Point to Point (P2P), but these are based on synthetic benchmarks rather than real‐world codes. In this work, we replace the existing non‐blocking P2P communication calls in the Met Office NERC Cloud model, a mature code for modeling the atmosphere, with MPI RMA. We describe our approach in detail and discuss the options taken for correctness and performance. Experiments are performed on ARCHER, a Cray XC30, and Cirrus, an SGI ICE machine. We demonstrate on ARCHER that, by using RMA, we can obtain between a 5% and 10% reduction in communication time at each timestep on up to 32768 cores, which over the entirety of a run (with many timesteps) results in a significant improvement in performance compared to P2P on the Cray. However, RMA is not a silver bullet, and there are challenges when integrating RMA calls into existing codes: important optimizations are necessary to achieve good performance and library support is not universally mature, as is the case on Cirrus. In this paper, we discuss, in the context of a real‐world code, the lessons learned converting P2P to RMA, explore performance and scaling challenges, and contrast alternative RMA synchronization approaches in detail.
Original language | English |
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Article number | e5008 |
Number of pages | 14 |
Journal | Concurrency and Computation: Practice and Experience |
Volume | 31 |
Issue number | 16 |
Early online date | 25 Sept 2018 |
DOIs | |
Publication status | E-pub ahead of print - 25 Sept 2018 |
Keywords / Materials (for Non-textual outputs)
- MPI RMA
- One sided communications
- MONC
- Cray XC30
- SGI ICE
- ARCHER
- Cirrus
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Met Office NERC Cloud model (MONC)
Brown, N. (Creator) & Weiland, M. (Creator), Edinburgh DataShare, 30 Apr 2018
DOI: 10.7488/ds/2343
Dataset