  We consider the recently proposed Coded Distributed Computing (CDC) framework
that leverages carefully designed redundant computations to enable coding
opportunities that substantially reduce the communication load of distributed
computing. We generalize this framework to heterogeneous systems where
different nodes in the computing cluster can have different storage (or
processing) capabilities. We provide the information-theoretically optimal data
set placement and coded data shuffling scheme that minimizes the communication
load in a cluster with 3 nodes. For clusters with $K>3$ nodes, we provide an
algorithm description to generalize our coding ideas to larger networks.
