NRG Ljubljana

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NRG Ljubljana 2026.09: Major New Release This release summarizes changes since the 2024.12 release, with major improvements in GPU accelerator support, performance, memory efficiency, physical capabilities, and workflows. CUDA And Numerical Performance NVIDIA CUDA support has been added for numerical linear algebra operations. CUDA-enabled builds can select diag=cuda to use cuSOLVER diagonalisation routines (real and complex). Selected operator-recalculation matrix products can be offloaded with mult=cuda. Eigenvector blocks and intermediate accumulators remain cached on the GPU during recalculation. BLAS/LAPACK integration has been improved, including support for ILP64 numerical libraries for really large problems. Threaded BLAS, MPI scheduling, and oversubscription diagnostics are handled more consistently. CPU diagonalization now defaults to the divide-and-conquer LAPACK routines dsyevd and zheevd. Performance And Memory All unnecessary matrix, eigenspace, and density-matrix copies have been eliminated. Multithreading in tools (kk, hilb, broaden). Seed operators, diagonalization data, and obsolete eigenvector representations are released from memory earlier. FDM and DMNRG spectral kernels now reuse weights, contractions, and existing operator-block information. FDM back-iteration exploits the diagonal structure of discarded-state density matrices. Memory requirements for diagonalization workspaces are now reported more clearly. Completed Wilson shells no longer redundantly retain full eigenvector and operator-block matrices. Thermodynamic histories and density-matrix back-iteration data now use separate, compact storage. New Physics Capabilities Multiple local phonon modes are supported, including independent bosonic cutoffs. Tensor-product construction of phonon bases and mode-resolved operators has been generalized. Superconducting Wilson chains can be instantiated using Nambu onsite and hopping coefficients. Symmetry triangle inequalities are enforced before constructing reduced operator matrix elements. Floquet-NRG support introduces Floquet bases and quasienergy-aware truncation. Three-channel QST calculations received important low-energy Hamiltonian and recalculation fixes. Orbital-triplet operator generation and several SNEG symbolic-algebra operations were corrected. Spectra And Observables report.nrg can list low-lying states together with diagonal observables, aiding fixed-point and sub-gap-state identification. broaden accepts arbitrary user-provided output-frequency meshes. adapt --flat Gamma directly supports constant hybridization functions. hilb, kk, integ, and resample can select GSL's monotonicity-preserving Steffen interpolation. integ is now a unified integrator. Thermal Fermi and Bose kernels are stable at extreme energies and near the Bose pole. FDM partition-function accumulators consistently retain high numerical precision. Level-flow energies can be reported in user-selected or physical energy units. Raw HDF5 output and broadening sum-rule diagnostics received correctness fixes. Workflows And Reliability Parameter files, spectral meshes, and truncated inputs now receive substantially stricter validation. The new instantiate/nrgspawn workflow can run prepared model templates without invoking Mathematica for every parameter point. Basis, Hamiltonian, and operator blocks can be saved and reused in parameter sweeps. Conda packaging and build coverage now include broader Linux, macOS, ARM, BLAS, and compiler configurations. Installation NRG Ljubljana is available from conda-forge: conda install -c conda-forge nrgljubljana Packages are available for Linux x86-64, Linux aarch64, macOS x86-64, and macOS arm64. Source builds remain appropriate when CUDA support or nonstandard numerical-library configurations are required. Requirements And Compatibility The C++ runtime requires a C++20 compiler. CPU diagonalization now defaults to the divide-and-conquer LAPACK routines dsyevd and zheevd. Numerical results should remain equivalent within floating-point tolerances. BLAS/LAPACK and OpenMP runtime selection is handled more explicitly. Users combining MPI with threaded numerical libraries should review their rank and thread settings to avoid oversubscription. CUDA support is optional and must be enabled explicitly in source builds. It is not currently included in the standard conda-forge packages.

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