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Code generation in ORCA: progress, efficiency and tight integration
Marvin H Lechner1, Anastasios Papadopoulos1, Kantharuban Sivalingam1
1Department of Molecular Theory and Spectroscopy, Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany. frank.neese@kofo.mpg.de.
Physical Chemistry Chemical Physics : PCCP
|May 20, 2024
Summary
ORCA-AGE II, an automated code generator, now uses C++ for significant performance gains and handles complex theories like fic-MRCC. This enhances computational chemistry research by automating code generation and reducing development time.
Area of Science:
- Computational Chemistry
- Quantum Chemistry
- Software Development
Background:
- The ORCA-AGE (automated generator environment) toolchain previously used Python for generating computational chemistry code.
- Developing complex quantum chemical theories requires significant programming effort and time.
Purpose of the Study:
- To present ORCA-AGE II, a significantly improved version of the automated code generator.
- To enhance performance, modularity, and integration with the ORCA quantum chemistry software.
- To enable the implementation of highly complex theoretical methods and automated gradient calculations.
Main Methods:
- Rewriting the ORCA-AGE toolchain in C++ for performance optimization.
- Implementing a modular design for easier integration of complex code engines.
- Achieving tight integration with the ORCA host program for automated code generation and compilation.
- Developing capabilities for generating parallelized production-level code for advanced theories like fic-MRCC.
Main Results:
- Achieved performance gains of up to two orders of magnitude compared to the Python version.
- Enabled the generation of parallelized code for highly complex theories, including fully internally contracted multireference coupled-cluster (fic-MRCC) theory.
- Demonstrated automated implementation of nuclear gradients for arbitrary theories.
- Ensured longevity and uniform code quality through automated generation and integration.
Conclusions:
- ORCA-AGE II significantly accelerates the development and implementation of advanced computational chemistry methods.
- The tool enables researchers to focus on theoretical development rather than implementation details.
- This advancement facilitates the study of theories previously too complex for practical implementation.
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