24717

ECM modeling and performance tuning of SpMV and Lattice QCD on A64FX

Christie Alappat, Nils Meyer, Jan Laukemann, Thomas Gruber, Georg Hager, Gerhard Wellein, Tilo Wettig
Erlangen Regional Computing Center, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
arXiv:2103.03013 [cs.PF], (4 Mar 2021)

@misc{alappat2021ecm,

   title={ECM modeling and performance tuning of SpMV and Lattice QCD on A64FX},

   author={Christie Alappat and Nils Meyer and Jan Laukemann and Thomas Gruber and Georg Hager and Gerhard Wellein and Tilo Wettig},

   year={2021},

   eprint={2103.03013},

   archivePrefix={arXiv},

   primaryClass={cs.PF}

}

The A64FX CPU is arguably the most powerful Arm-based processor design to date. Although it is a traditional cache-based multicore processor, its peak performance and memory bandwidth rival accelerator devices. A good understanding of its performance features is of paramount importance for developers who wish to leverage its full potential. We present an architectural analysis of the A64FX used in the Fujitsu FX1000 supercomputer at a level of detail that allows for the construction of Execution-Cache-Memory (ECM) performance models for steady-state loops. In the process we identify architectural peculiarities that point to viable generic optimization strategies. After validating the model using simple streaming loops we apply the insight gained to sparse matrix-vector multiplication (SpMV) and the domain wall (DW) kernel from quantum chromodynamics (QCD). For SpMV we show why the CRS matrix storage format is not a good practical choice on this architecture and how the SELL-C-σ format can achieve bandwidth saturation. For the DW kernel we provide a cache-reuse analysis and show how an appropriate choice of data layout for complex arrays can realize memory-bandwidth saturation in this case as well. A comparison with state-of-the-art high-end Intel Cascade Lake AP and Nvidia V100 systems puts the capabilities of the A64FX into perspective. We also explore the potential for power optimizations using the tuning knobs provided by the Fugaku system, achieving energy savings of about 31% for SpMV and 18% for DW.
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