The QISG suite: high-performance codes for studying Quantum Ising Spin Glasses
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910319612264448 |
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| author | Bernaschi, Massimo Pemartín, Isidoro González-Adalid Martín-Mayor, Víctor Parisi, Giorgio |
| author_facet | Bernaschi, Massimo Pemartín, Isidoro González-Adalid Martín-Mayor, Víctor Parisi, Giorgio |
| contents | We release a set of GPU programs for the study of the Quantum ($S=1/2$) Spin Glass on a square lattice, with binary couplings. The library contains two main codes: MCQSG (that carries out Monte Carlo simulations using both the Metropolis and the Parallel Tempering algorithms, for the problem formulated in the Trotter-Suzuki approximation), and EDQSG (that obtains the extremal eigenvalues of the Transfer Matrix using the Lanczos algorithm). EDQSG has allowed us to diagonalize transfer matrices with size up to $2^{36}\times2^{36}$. From its side, MCQSG running on four NVIDIA A100 cards delivers a sub-picosecond time per spin-update, a performance that is competitive with dedicated hardware. We include as well in our library GPU programs for the analysis of the spin configurations generated by MCQSG. Finally, we provide two auxiliary codes: the first generates the lookup tables employed by the random number generator of MCQSG; the second one simplifies the execution of multiple runs using different input data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03920 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The QISG suite: high-performance codes for studying Quantum Ising Spin Glasses Bernaschi, Massimo Pemartín, Isidoro González-Adalid Martín-Mayor, Víctor Parisi, Giorgio Computational Physics Disordered Systems and Neural Networks We release a set of GPU programs for the study of the Quantum ($S=1/2$) Spin Glass on a square lattice, with binary couplings. The library contains two main codes: MCQSG (that carries out Monte Carlo simulations using both the Metropolis and the Parallel Tempering algorithms, for the problem formulated in the Trotter-Suzuki approximation), and EDQSG (that obtains the extremal eigenvalues of the Transfer Matrix using the Lanczos algorithm). EDQSG has allowed us to diagonalize transfer matrices with size up to $2^{36}\times2^{36}$. From its side, MCQSG running on four NVIDIA A100 cards delivers a sub-picosecond time per spin-update, a performance that is competitive with dedicated hardware. We include as well in our library GPU programs for the analysis of the spin configurations generated by MCQSG. Finally, we provide two auxiliary codes: the first generates the lookup tables employed by the random number generator of MCQSG; the second one simplifies the execution of multiple runs using different input data. |
| title | The QISG suite: high-performance codes for studying Quantum Ising Spin Glasses |
| topic | Computational Physics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2402.03920 |