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- W4286268954 abstract "We present ExaTN (Exascale Tensor Networks), a scalable GPU-accelerated C++ library which can express and process tensor networks on shared- as well as distributed-memory high-performance computing platforms, including those equipped with GPU accelerators. Specifically, ExaTN provides the ability to build, transform, and numerically evaluate tensor networks with arbitrary graph structures and complexity. It also provides algorithmic primitives for the optimization of tensor factors inside a given tensor network in order to find an extremum of a chosen tensor network functional, which is one of the key numerical procedures in quantum many-body theory and quantum-inspired machine learning. Numerical primitives exposed by ExaTN provide the foundation for composing rather complex tensor network algorithms. We enumerate multiple application domains which can benefit from the capabilities of our library, including condensed matter physics, quantum chemistry, quantum circuit simulations, as well as quantum and classical machine learning, for some of which we provide preliminary demonstrations and performance benchmarks just to emphasize a broad utility of our library." @default.
- W4286268954 created "2022-07-21" @default.
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- W4286268954 date "2022-07-06" @default.
- W4286268954 modified "2023-10-01" @default.
- W4286268954 title "ExaTN: Scalable GPU-Accelerated High-Performance Processing of General Tensor Networks at Exascale" @default.
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- W4286268954 doi "https://doi.org/10.3389/fams.2022.838601" @default.
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