Compiler Support for Message Passing Systems

1998 ◽  
Author(s):  
Jens Nielsen
Keyword(s):  
Message Passing ◽  
Compiler Support

HPF LIBRARY AND COMPILER SUPPORT FOR HALOS IN DATA PARALLEL IRREGULAR COMPUTATIONS

2000 ◽  
Vol 10 (02n03) ◽  
pp. 189-200 ◽  
Author(s):  
THOMAS BRANDES
Keyword(s):  
Message Passing ◽  
High Performance ◽  
Parallel Programs ◽  
Address Space ◽  
Compiler Support ◽  
Data Parallel ◽  
High Performance Fortran ◽  
Non Local ◽  
Performance Results ◽  
Memory Architectures

On distributed memory architectures data parallel compilers emulate the global address space by distributing the data onto the processors according to the mapping directives of the user and by generating automatically explicit inter-processor communication. A shadow is additionally allocated local memory to keep on one processor also non-local values of the data that is accessed or defined by this processor. While shadow edges are already well studied for structured grids, this paper focuses on its use for applications with unstructured grids where updates on the shadow edges involve unstructured communication with complex communication schedules. The use of shadow edges is considered for High Performance Fortran (HPF) as the de facto standard language for writing data parallel programs in Fortran. A library with a HPF binding provides the explicit control of unstructured shadows and their communication schedules, also called halos. This halo library allows writing HPF programs with a performance close to hand-coded message-passing versions but where the user is freed of the burden to calculate shadow sizes and communication schedules and to do the exchanging of data with explicit message passing commands. In certain situations, the HPF compiler can create and use halos automatically. This paper shows the advantages and also the limits of this approach. The halo library and an automatic support of halos have been implemented within the ADAPTOR HPF compilation system. The performance results verify the effectiveness of the chosen approach.

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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