High Performance Monte Carlo Simulation of Ising Model on TPU Clusters

Large-scale deep learning benefits from an emerging class of AI accelerators. Some of these accelerators'designs are general enough for compute-intensive applications beyond AI and Cloud TPU is one such example. In this paper, we demonstrate a novel approach using TensorFlow on Cloud TPU to sim...

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Veröffentlicht in:SC19: International Conference for High Performance Computing, Networking, Storage and Analysis S. 1 - 15
Hauptverfasser: Yang, Kun, Chen, Yi-Fan, Roumpos, Georgios, Colby, Chris, Anderson, John
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 17.11.2019
Schlagworte:
Adaptation models
Benchmark testing
Cloud TPU
Codes
Computational modeling
Ising Model
Magnetization
Markov Chain Monte Carlo
Monte Carlo methods
Numerical models
Simulation
Solid modeling
ISSN:2167-4337
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Large-scale deep learning benefits from an emerging class of AI accelerators. Some of these accelerators'designs are general enough for compute-intensive applications beyond AI and Cloud TPU is one such example. In this paper, we demonstrate a novel approach using TensorFlow on Cloud TPU to simulate the two-dimensional Ising Model. TensorFlow and Cloud TPU framework enable the simple and readable code to express the complicated distributed algorithm without compromising the performance. Our code implementation fits into a small Jupyter Notebook and fully utilizes Cloud TPU's efficient matrix operation and dedicated high speed inter-chip connection. The performance is highly competitive: it outperforms the best published benchmarks to our knowledge by 60% in single-core and 250% in multi-core with good linear scaling. When compared to Tesla V100 GPU, the single-core performance maintains a ~10% gain. We also demonstrate that using low precision arithmetic-bfloat16-does not compromise the correctness of the simulation results.
ISSN:2167-4337
DOI:10.1145/3295500.3356149