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| 001 | 1044949 | ||
| 005 | 20251030202115.0 | ||
| 024 | 7 | _ | |a 10.1007/s11227-025-07145-6 |2 doi |
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| 100 | 1 | _ | |a Baumeister, Paul F. |0 P:(DE-Juel1)156619 |b 0 |e Corresponding author |
| 245 | _ | _ | |a tfQMRgpu: a GPU-accelerated linear solver with block-sparse complex result matrix |
| 260 | _ | _ | |a Dordrecht [u.a.] |c 2025 |b Springer Science + Business Media B.V |
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| 520 | _ | _ | |a We present tfQMRgpu, a GPU-accelerated iterative linear solver based on the transpose-free quasi-minimal residual (tfQMR) method. Designed for large-scale electronic structure calculations, particularly in the context of Korringa–Kohn–Rostoker density functional theory, tfQMRgpu efficiently handles block-sparse complex matrices arising from multiple scattering theory. The solver exploits GPU parallelism to accelerate convergence while leveraging memory-efficient sparse storage formats. By unifying the solution of multiple right-hand side (RHS) block vectors, tfQMRgpu significantly improves throughput, demonstrating up to a speedup on modern GPUs. Additionally, we introduce a flexible implementation framework that supports both explicit matrix-based and matrix-free operator formulations, such as high-order finite-difference stencils for real-space grid-based Green function calculations. Benchmarks on various NVIDIA GPUs demonstrate the solver’s efficiency, in some cases achieving over 56% of peak floating-point performance for block-sparse matrix multiplications. tfQMRgpu is open-source, providing interfaces for C, C++, Fortran, Julia, and Python, making it a versatile tool for high-performance computing applications that can benefit from the unification of RHS problems. |
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| 773 | _ | _ | |a 10.1007/s11227-025-07145-6 |g Vol. 81, no. 5, p. 663 |0 PERI:(DE-600)1479917-0 |n 5 |p 663 |t The journal of supercomputing |v 81 |y 2025 |x 0920-8542 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1044949/files/s11227-025-07145-6.pdf |y OpenAccess |
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