FALCON: Efficient Designs for Zero-Copy MPI Datatype Processing on Emerging Architectures

Derived datatypes are commonly used in MPI applications to exchange non-contiguous data among processes. However, state-of-the-art MPI libraries do not offer efficient processing of derived datatypes and often rely on packing and unpacking the data at the sender and the receiver processes. This approach incurs the cost of extra copies and increases overall communication latency. While zero-copy communication schemes have been proposed for contiguous data, applying such techniques to non-contiguous data transfers bring forth several new challenges. In this work, we address these challenges and propose FALCON — Fast and Low-overhead Communication designs for intra-node MPI derived datatypes processing. We show that the memory layouts translation of derived datatypes introduce significant overheads in the communication path and propose novel solutions to mitigate such bottlenecks. We also find that the current MPI datatype routines cannot fully take advantage of the zero-copy mechanisms, and propose enhancements to the MPI standard to address these limitations. The experimental evaluations show that our proposed designs achieve up to 3 times improved intra-node communication latency and bandwidth over state-of-the-art MPI libraries. We also evaluate our designs with communication kernels of popular scientific applications such as MILC, WRF, NAS MG, and 3D-Stencil on three different multi-/many-core architectures and show up to 5.5 times improvement over state-of-the-art designs employed by production MPI libraries.