Optimizing Communication Scheduling Using Dataflow Semantics

We show how coarse grain dataflow semantics (CGD) applied to SPMD algorithms makes application development and design space exploration simpler compared to message passing, at the same time providing on par performance. CGD applications are specified as dependencies between computation modules and data distributions. Communication and synchronization are added automatically and optimized for specific architectures, relieving programmers of this task. Many high level algorithm changes are easy to implement in CGD by redefining data distributions. These include exposing communication overlap by decreasing task grain, and aggregating communication by replicating data and computation. We briefly present a coordination language with dataflow semantics that implements the CGD model. Our implementation currently supports MPI, SHMEM, and pthreads. Results on Altix 4700 show our optimized CGD FT is 27% faster than original NPB 2.3 MPI implementation, and optimized CGD stencil has a 41% advantage over handwritten MPI.