  Density functionals at the level of the Generalized Gradient Approximation
(GGA) and a plane-wave basis set are widely used today to perform ab initio
molecular dynamics (AIMD) simulations. Going up in the ladder of accuracy of
density functionals from GGA (2nd rung) to hybrid density functionals (4th
rung) is much desired pertaining to the accuracy of the latter in describing
structure, dynamics, and energetics of molecular and condensed matter systems.
On the other hand, hybrid density functional based AIMD simulations are about
two orders of magnitude slower than GGA based AIMD for systems containing ~100
atoms using ~100 compute cores. Two methods, namely MTACE and s-MTACE, based on
a multiple time step integrator and adaptively compressed exchange operator
formalism are able to provide a speed-up of about 7-9 in performing hybrid
density functional based AIMD. In this work, we report an implementation of
these methods using a task-group based parallelization within the CPMD program
package, with the intention to take advantage of the large number of compute
cores available on modern high-performance computing platforms. We present here
the boost in performance achieved through this algorithm. This work also
identifies the computational bottleneck in the s-MTACE method, and proposes a
way to overcome that.
