A High-Performance Design for Hierarchical Parallelism in the QMCPACK Monte Carlo Code
DescriptionWe introduce a new high-performance design for parallelism within the Quantum Monte Carlo code QMCPACK. We demonstrate that the new design is better able to exploit the hierarchical parallelism of heterogeneous architectures compared to the previous GPU implementation. The new version is able to achieve higher GPU occupancy via the new concept of crowds of Monte Carlo walkers, and by enabling more host CPU threads to effectively offload to the GPU. The higher performance is expected to be achieved independent of the underlying hardware, significantly improving developer productivity and reducing code maintenance costs. Scientific productivity is also improved with full support for fallback to CPU execution when GPU implementations are not available or CPU execution is more optimal.
Event Type
Workshop
TimeFriday, 18 November 202210:50am - 11:10am CST
LocationC140-142
W
Algorithms
Architectures
Compilers
Computational Science
Exascale Computing
Heterogeneous Systems
Hierarchical Parallelism
Memory Systems
Parallel Programming Languages and Models
Parallel Programming Systems
Resource Management and Scheduling
Recorded