Mpi: a Standard Message Passing Interface 1 Mpi: a Standard Message Passing Interface

By D. Walker, Jack J. Dongarra. 1996

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Cong Xie
2. Oluwasanmi Koyejo
3. Indranil Gupta
4. Pantelis R. Vlachas
5. Jaideep Pathak
6. Brian R. Hunt
7. Xuelong Li
8. Haibin Lin

Citations in the corpus

• Local AdaAlter: Communication-Efficient Stochastic Gradient Descent with Adaptive Learning Rates (2019, methodology): “A common alternative approach of PS is the AllReduce algorithm, which is typically implemented by MPI (Sergeev and Balso, 2018; Walker and Dongarra, 1996).”
• Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics (2019, methodology): “The elements of the parallel architecture are trained independently, while the MPI (Dalcín, Paz, Kler, & Cosimo, 2011; Dalcín, Paz, Storti, & D’Elía, 2008; Walker & Dongarra, 1996) communication protocol is utilized to communicate the elements of the interaction for long-term forecasting.”
• Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, methodology): “However, it is difficult for traditional parallel models (like OpenMP [166] and MPI [167]) to implement parallel programs on a big data”
• CSER: Communication-efficient SGD with Error Reset (2020, methodology): “Thus, GRBS is compatible with AllReduce [18, 23] and parameter server [7, 11, 12].”