Electronic structure of nanotubes by utilizing the helical symmetry properties: The code optimization

Electronic structure of nanotubes by utilizing the helical symmetry properties: The code optimization
General case nanotube with helical translational symmetry.

Project reference: 1904

In calculations of nanotubes prevail methods based on a one-dimensional translational symmetry using a huge unit cell. A pseudo two-dimensional approach, when the inherent helical symmetry of general chirality nanotubes is exploited, has been limited to simple approximate model Hamiltonians. Currently, we are developing a new unique code for fully ab initio calculations of nanotubes that explicitly uses the helical symmetry properties, even for systems that are aperiodic in one dimension. Implementation is based on a formulation in two-dimensional reciprocal space where one dimension is continuous whereas the second one is discrete. Independent particle quantum chemistry methods, such as Hartee-Fock and/or DFT or simple post Hartree-Fock  perturbation techniques, such as Moller-Plesset 2nd order theory, are used to calculate the band structures.

The student is expected to cooperate on the optimization of this newly developed implementation and/or on a performance testing for simple general nanotube model cases. Message Passing Interface (MPI) was used as the primary tool in the code parallelization.

The aim of this work is to improve MPI parallelization in the recently developed parts of the code that include electron correlation effects. The second level parallelization in inner loops over the processors of individual nodes would certainly enhance the performance together with using threaded BLAS routines.

By improving the performance of our new software we will open up new possibilities for tractable highly accurate calculations of energies and band structures for nanotubes with predictive power and with facilitated band topology whose interpretation is much more transparent than in the conventionally used one-dimensional approach. We believe that this methodology soon becomes a standard tool for in silico design and investigation in both the academic and commercial sectors.

General case nanotube with helical translational symmetry.

Project Mentor: Prof. Dr. Jozef Noga, DrSc.

Project Co-mentor: Ing. Marian Gall, PhD.

Site Co-ordinator: Mgr. Lukáš Demovič, PhD.

Participant: Irén Simkó

Learning Outcomes:
The student will familiarize himself with MPI programming and testing, as well as with ideas of efficient implementation of complex tensor-contraction based HPC applications. A basic understanding of treating the helical periodic systems in physics will be gained along with detailed knowledge of profiling tools and parallelization techniques.

Student Prerequisites (compulsory):
Basic knowledge of Fortran and MPI.

Student Prerequisites (desirable):
Advanced knowledge of C/C++ or Fortran and MPI, BLAS libraries and other HPC tools.

Training Materials:
Articles and test examples to be provided according to an actual student’s profile and skills.


  • Weeks 1-3: training; profiling of the software and design simple MPI implementation, Deliver Plan at the end of week 3
  • Weeks 4-7: optimization and extensive testing/benchmarking of the code
  • Week 8: report completion and presentation preparation

Final Product Description:
The resulting code will be capable of successfully and more efficiently completing the electronic structure calculations of nanotubes with a much simplified and transparent topology of the band structure.

Adapting the Project: Increasing the Difficulty:
A more efficient implementation with the hybrid model using both MPI and Open Multi-Processing  (OpenMP)

Adapting the Project: Decreasing the Difficulty:
Profiling of the code to provide key information on the bottlenecks and a simple MPI parallelization of the main loops.

The student will have access to the necessary learning material, as well as to our local IBM P775 supercomputer and x86 infiniband clusters. The software stack we plan to use is open source.

Computing Centre of the Slovak Academy of Sciences

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