  The recently developed semi-Lagrangian discontinuous Galerkin approach is
used to discretize hyperbolic partial differential equations (usually first
order equations). Since these methods are conservative, local in space, and
able to limit numerical diffusion, they are considered a promising alternative
to more traditional semi-Lagrangian schemes (which are usually based on
polynomial or spline interpolation).
  In this paper, we consider a parallel implementation of a semi-Lagrangian
discontinuous Galerkin method for distributed memory systems (so-called
clusters). Both strong and weak scaling studies are performed on the Vienna
Scientific Cluster 2 (VSC-2). In the case of weak scaling, up to 8192 cores, we
observe a parallel efficiency above 0.89 for both two and four dimensional
problems. Strong scaling results show good scalability to at least 1024 cores
(we consider problems that can be run on a single processor in reasonable
time). In addition, we study the scaling of a two dimensional Vlasov--Poisson
solver that is implemented using the framework provided. All of the simulation
are conducted in the context of worst case communication overhead; i.e., in a
setting where the CFL number increases linearly with the problem size.
  The framework introduced in this paper facilitates a dimension independent
implementation (based on C++ templates) of scientific codes using both an MPI
and a hybrid approach to parallelization. We describe the essential ingredients
of our implementation.
