4 research outputs found
Exploring Fully Offloaded GPU Stream-Aware Message Passing
Modern heterogeneous supercomputing systems are comprised of CPUs, GPUs, and
high-speed network interconnects. Communication libraries supporting efficient
data transfers involving memory buffers from the GPU memory typically require
the CPU to orchestrate the data transfer operations. A new offload-friendly
communication strategy, stream-triggered (ST) communication, was explored to
allow offloading the synchronization and data movement operations from the CPU
to the GPU. A Message Passing Interface (MPI) one-sided active target
synchronization based implementation was used as an exemplar to illustrate the
proposed strategy. A latency-sensitive nearest neighbor microbenchmark was used
to explore the various performance aspects of the implementation. The offloaded
implementation shows significant on-node performance advantages over standard
MPI active RMA (36%) and point-to-point (61%) communication. The current
multi-node improvement is less (23% faster than standard active RMA but 11%
slower than point-to-point), but plans are in progress to purse further
improvements.Comment: 12 pages, 17 figure
Designing Multi-Leader-Based Allgather Algorithms for Multi-Core Clusters
The increasing demand for computational cycles is being met by the use of multi-core processors. Having large number of cores per node necessitates multi-core aware designs to extract the best performance. The Message Passing Interface (MPI) is the dominant parallel programming model on modern high performance computing clusters. The MPI collective operations take a significant portion of the communication time for an application. The existing optimizations for collectives exploit shared memory for intranode communication to improve performance. However, it still would not scale well as the number of cores per node increase. In this work, we propose a novel and scalable multileader-based hierarchical Allgather design. This design allows better cache sharing for Non-Uniform Memory Access (NUMA) machines and makes better use of the network speed available with high performance interconnects such as InfiniBand. The new multi-leader-based scheme achieves a performance improvement of up to 58 % for small messages and 70 % for medium sized messages