Generating coupled cluster code for modern distributed memory tensor software
Jan Brandejs, Johann Pototschnig, Trond Saue

TL;DR
This paper introduces a general-order coupled cluster code generator that efficiently produces high-performance, scalable code for distributed memory tensor computations on GPU HPC platforms, facilitating advanced quantum chemistry calculations.
Contribution
The authors developed a modular, open-source tensor framework 'tenpi' and a code generator that captures tensor symmetries, enabling scalable coupled cluster computations on thousands of GPUs.
Findings
Code scales efficiently up to 1200 GPUs
Supports higher-order coupled cluster methods
Integrates with DIRAC for relativistic molecular calculations
Abstract
Using GPU-based HPC platforms efficiently for coupled cluster computations is a challenge due to heterogeneous hardware structures. The constant need to adapt software to these structures and the required man-hours makes a systematization of high-performance code development desirable, even more so for higher-order coupled cluster. This is generally achieved by introducing a high-level representation of the problem, which is then translated to low-level instructions for the hardware using a compiler/translator component. Designing such software comes with another challenge: Allowing efficient implementation by capturing key symmetries of tensors, while retaining the abstraction from the hardware. We review ways to address these two challenges while presenting design decisions which led us to the development of a general-order coupled cluster code generator. The systematically produced…
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Taxonomy
TopicsDistributed and Parallel Computing Systems · Parallel Computing and Optimization Techniques · Computational Physics and Python Applications
