ZeroSpy: Exploring Software Inefficiency with Redundant Zeros

You, Xiao‐Zeng; Yang, Hailong; Luan, Zhongzhi; Qian, Depei; Liu, Xu

doi:10.1109/sc41405.2020.00033

Cited by 7 publications

(1 citation statement)

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“…The data movement of evaluating the descriptor D 𝑖 on typical modern many-core architecture is illustrated in Removing redundancy is the second strategy used in our optimization. Related work demonstrated that up to 52 percent of performance boost can be gained in removing calculations related to redundant zeros [36]. In the previous DP model, the data redundancy stems from the padding of the number of rows of the environment matrix R𝑖 to 𝑁 𝑚 , where 𝑁 𝑚 is the maximally possible length of atomic neighboring list as discussed in Sec.…”

Section: Optimization Strategymentioning

confidence: 99%

Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms

Guo

Yan

et al. 2022

Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of ab initio molecular dynamics in terms of spatial and temporal scales on modern supercomputers. Previous state-of-the-art can achieve 1 − 2 nanoseconds molecular dynamics simulation per day for 100-million atoms on the entire Summit supercomputer. In this paper, we have significantly reduced the memory footprint and computational time by a comprehensive approach with both algorithmic and system innovations. The neural network model is compressed by model tabulation, kernel fusion, and redundancy removal. Then optimizations such as acceleration of customized kernel, tabulation of activation function, MPI+OpenMP parallelization are implemented on GPU and ARM architectures. Testing results of the copper system show that the optimized code can scale up to the entire machine of both Fugaku and Summit, and the corresponding system size can be extended by a factor of 134 to an Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for thirdparty components of this work must be honored. For all other uses, contact the owner/author(s).

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Section: Optimization Strategymentioning

confidence: 99%