1. Interface HFBTHO/HFODD and Comments on Parallelization UTK-ORNL DFT group

2. Interface HFBTHO/HFODD HFBTHO Cylindrical HO Basis Axial symmetry and time-reversal symmetry HFODD Cartesian HO basis Symmetry unrestricted Principle Unitary transformation Cylindrical to Cartesian Phase transformation Tweak HFODD to restart from HFB matrix elements instead of density fields on Gauss-Hermite mesh  New HFODD and MPI_HFODD versions with HFBTHO as a module called (upon request) in initial stage  Automatic restart of HFODD  I/O required: HFB matrix + basis quantum numbers written/read on disk Open Issues:  too much memory required for large (N ≥ 18 shells) deformed bases  more tests for odd nuclei

3. MPI_HFODD  Master-slave architecture: master defines a list of task, distributes the tasks to the slaves available (until list is empty) and collect the results  Compiles/runs with Intel Fortran, GNU Fortran, Portland and PathScale compilers  Can run on your laptop…! (most modern laptops are dual/quad cores) Super Computers: Increase number of cores at fixed memory  Available memory per core is decreasing ! 90% of CPU-time taken by only two subroutines:  DENSHF (calculation of fields on Gauss-Hermite mesh)  DIAMAT (diagonalization of HFB matrix

4. Future of HFODD Applications on Leadership Class computers Future work:  Diagonalization of the HFB matrix can be parallelized “relatively” simply by the use of threading and ARPACK or ScaLAPACK specialized routines  Parallelization of density fields is more tricky  Include HFODD - MPI_HFODD in optimization codes  Asynchronous Dynamic Load Balancing (ADLB – UNEDF project): dynamic stack. List of task is updated on the fly based on results Good practice in programming  Remember: memory is expensive, CPU-time is fast and cheap  Use Fortran 90 for dynamic memory allocation  Avoid vectorization and think parallelization instead Example: Takagi factorization should decrease by ~4 the memory needed