Implementation and Evaluation of Network Interface and Message Passing Services for TianHe-1A Supercomputer

2011 ◽  
Author(s):  
Min Xie ◽  
Yutong Lu ◽  
Lu Liu ◽  
Hongjia Cao ◽  
Xuejun Yang
Keyword(s):  
Message Passing ◽  
Network Interface

TRIPWIRE: A SYNCHRONISATION PRIMITIVE FOR VIRTUAL MEMORY MAPPED COMMUNICATION

2001 ◽  
Vol 02 (03) ◽  
pp. 345-364 ◽  
Author(s):  
DAVID RIDDOCH ◽  
STEVE POPE ◽  
DEREK ROBERTS ◽  
GLENFORD MAPP ◽  
DAVID CLARKE ◽  
...  
Keyword(s):  
Message Passing ◽  
Data Transfer ◽  
Distributed Shared Memory ◽  
Virtual Memory ◽  
Communication Styles ◽  
Network Interface ◽  
High Bandwidth ◽  
Memory Network ◽  
Network Abstraction ◽  
Network Interfaces

Existing user-level network interfaces deliver high bandwidth, low latency performance to applications, but are typically unable to support diverse styles of communication and are unsuitable for use in multiprogrammed environments. Often this is because the network abstraction is presented at too high a level, and support for synchronisation is inflexible. In this paper we present a new primitive for in-band synchronisation: the Tripwire. Tripwires provide a flexible, efficient and scalable means for synchronisation that is orthogonal to data transfer. We describe the implementation of a non-coherent distributed shared memory network interface, with Tripwires for synchronisation. This interface provides a low-level communications model with gigabit class bandwidth and very low overhead and latency. We show how it supports a variety of communication styles, including remote procedure call, message passing and streaming.

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

2020 ◽  
Author(s):  
Ali Raza ◽  
Arni Sturluson ◽  
Cory Simon ◽  
Xiaoli Fern
Keyword(s):  
Electronic Structure ◽  
Message Passing ◽  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Computational Cost ◽  
Electronic Structure Calculations ◽  
High Fidelity ◽  
Partial Charges ◽  
Metal Organic ◽  
Structure Calculations

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.<br>

Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction

2019 ◽  
Author(s):  
Michael Withnall ◽  
Edvard Lindelöf ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
Message Passing ◽  
State Of The Art ◽  
A Priori ◽  
Model Performance ◽  
Learning Approaches ◽  
Physical Chemical ◽  
Chemical Descriptor ◽  
Derived Properties ◽  
Memory Schemes ◽  
Hyperparameter Selection

We introduce Attention and Edge Memory schemes to the existing Message Passing Neural Network framework for graph convolution, and benchmark our approaches against eight different physical-chemical and bioactivity datasets from the literature. We remove the need to introduce <i>a priori</i> knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.

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