A novel Jacobi-type tensor algorithm for tensor decomposition is investigated and a CUDA-based algorithm that supports tensors of arbitrary order on a single GPU is developed that outperforms the original MATLAB reference.
Abstract
Quantum systems grow in complexity so rapidly that even modest models become difficult to simulate, creating a strong need for methods that can handle high-dimensional data, also known as tensors. In this work, we investigate a novel Jacobi-type tensor algorithm for tensor decomposition and develop a CUDA-based algorithm that supports tensors of arbitrary order on a single GPU. We test the implementation on NVIDIA H100 GPUs and show that the algorithm converges correctly for diagonalizable tensors up to nine dimensions, with runtime scaling in a predictable way as tensor order grows. Finally, our general algorithm outperforms the original MATLAB reference by more than two orders of magnitude.
We present a fully GPU-oriented SVD pipeline based on polar decomposition, motivated by iterative methods that rely solely on matrix multiplications, such as the Newton-Schulz iteration. We show that this approach enables up to a $2\times$ speedup compared to standard implementations. Furthermore, we derive a numerical...
Uliana Parkina, Askar Tsyganov, Sergei Kudriashov et al.· 0 citations
This work shows how to achieve vectorization for the practically relevant case of abelian symmetric tensor networks (which includes fermionic tensor networks) by developing a ``flat''tensor network formalism for block-sparse tensor representation and contraction.
Si-Jing Du, Johnnie Gray, Ao Chen et al.· 0 citations
The rapid evolution of 5G Advanced and 6G networks necessitates real-time direction-of-arrival (DOA) estimation within Massive MIMO systems. Traditionally, the computational complexity of subspace-based algorithms like MUSIC has required low-level C++ implementations or specialized FPGA hardware to overcome the perform...
O. Polikarovskykh, I. Hula, A. Kashtalian· Automation, Control, and Inf...· 0 citations
We present trainsum, a Python package for computations with multidimensional quantics tensor trains. The package enables efficient approximation of tensors and functions by tensor trains, regardless of their shape or dimensionality, and supports analytic constructions of important objects, such as polynomials and Fouri...
Paul Haubenwallner, Matthias Heller· SciPost Physics Codebases· 0 citations
Machine-learned interatomic potentials extend atomistic simulations beyond the length- and timescales accessible to electronic-structure methods. However, the computational cost of equivariant architectures limits the local correlations they can represent in practice and therefore their achievable accuracy. Here we int...
V. Zaverkin, P. Goodarzi, S. Sukhomlinov et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
