Computational Chemistry and Distributed Computing

Dr Manolo Per1, Dr Deidre Cleland1

1Data61 CSIRO, Docklands, Australia

 

Accurate prediction of the quantum-scale properties of materials and chemical processes requires a significant amount of computational resources. The methods commonly used need large amounts of memory and fast network interconnects, and so depend on the use of supercomputers for practical applications. As a result, a considerable fraction of the world’s supercomputer time is consumed by these calculations.

However, these methods are unable to make use of the very large numbers of compute cores which are common in contemporary supercomputers. More important issues arise as we head toward the Exascale era, where the emergence of architectures using heterogeneous solutions such as accelerators present additional challenges to both theory and software implementation.

In this talk I will describe our approach to solving these problems, which involves a very specific implementation of a stochastic power method for solving the quantum mechanical Schrodinger equation. The resulting algorithm provides almost perfect scalability, enables the use of heterogeneous environments, is implicitly fault tolerant, and can be used in truly distributed environments with very slow network connections.

These developments give the unique ability to exploit virtually any computational resource, including current and future supercomputers, idle PCs, and commercial clouds


Biography:

Dr. Manolo Per leads Data61’s Molecular and Materials Modelling Team, an inter-disciplinary team of computational scientists who develop and implement new techniques in fundamental and data-driven chemical and materials science.

Manolo’s background is in electronic-structure theory, and his main research interests are in the development of methods for solving the quantum many-body problem, and their practical implementation in computer software.

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