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Nuclear shell-model code for massive parallel computation, "KSHELL"

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arxiv 1310.5431 v1 pith:HILUHRRK submitted 2013-10-21 nucl-th

classification nucl-th
keywords parallelcodecomputationlanczosshell-modeldevelopedkshellmethod
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

A new code for nuclear shell-model calculations, "KSHELL", is developed. It aims at carrying out both massively parallel computation and single-node computation in the same manner. We solve the Schr\"{o}dinger's equation in the $M$-scheme shell-model model space, utilizing Thick-Restart Lanczos method. During the Lanczos iteration, the whole Hamiltonian matrix elements are generated "on-the-fly" in every matrix-vector multiplication. The vectors of the Lanczos method are distributed and stored on memory of each parallel node. We report that the newly developed code has high parallel efficiency on FX10 supercomputer and a PC with multi-cores.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Absence of a shell closure in $^{140}$Sn

    nucl-th 2026-07 accept novelty 6.0 of 10

    Chiral-EFT ab initio computations yield a small 2+ energy in 140Sn under a closed 7/2- subshell assumption, contradicting that shell closure.

  2. Nuclear Many-Body Systems as Benchmarks for Quantum Computing

    quant-ph 2026-07 conditional novelty 5.0 of 10

    A unified workflow maps realistic nuclear Hamiltonians to qubit operators and benchmarks three quantum eigenvalue algorithms, finding T-gate counts of 10^10–10^14 for valence-space QPE at hundreds of qubits.

  3. Nuclear Many-Body Systems as Benchmarks for Quantum Computing

    quant-ph 2026-07 conditional novelty 5.0 of 10

    NuQuLib maps realistic nuclear Hamiltonians to qubit Hamiltonians and compares T-gate costs of QPE, QKrylov, and ODMD across valence and no-core model spaces.

  4. Shell Model Calculations for Proton-rich Zn Isotopes via New Generated Effective Interaction by Artificial Neural Networks

    nucl-th 2019-07 unverdicted novelty 5.0 of 10

    An artificial neural network generates a new effective interaction jj44b_nn from jj44b, yielding shell model results for proton-rich Zn isotopes that are comparable to the original and closer to experiment in some cases.

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