Even though Dask and Spark are great technologies widely used in the IT industry, they have not been widely adopted in academic research. High-performance supercomputers with thousands of processors have been used in academia for decades to run intense numerical applications. For this reason, supercomputers are generally configured using a very different software stack that focuses on a computationally-intensive algorithm implemented in a low-level language, such as C, Fortran, or even assembly.
The principal library used for parallel execution on these kinds of systems is Message Passing Interface (MPI), which, while less convenient or sophisticated than Dask or Spark, is perfectly capable of expressing parallel algorithms and achieving excellent performance. Note that, contrary to Dask and Spark, MPI does not follow the MapReduce model and is best used for running thousands of...
