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One of the major challenges in parallelization is the difficulty of improving application scalability with conventional techniques. HPX provides efficient scalable parallelism by significantly reducing node starvation and effective latencies while controlling the overheads. In this paper, we present a new highly scalable parallel distributed N-Body application using a future-based algorithm, which is implemented with HPX. The main difference between this algorithm and prior art is that a future-based request buffer is used between different nodes and along each spatial direction to send/receive data to/from the remote nodes, which helps removing synchronization barriers. HPX provides an asynchronous programming model which results in improving the parallel performance. The results of using HPX for parallelizing Octree construction on one node and the force computation on the distributed nodes show the scalability improvement on an average by about 45% compared to an equivalent OpenMP implementation and 28% compared to a hybrid implementation (MPI+OpenMP) [1] respectively for one billion particles running on up to 128 nodes with 20 cores per each.
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PREEMPT: Scalable Epidemic Interventions Using Submodular Optimization on Multi-GPU Systems
Preventing and slowing the spread of epidemics is achieved through techniques such as vaccination and social distancing. Given practical limitations on the number of vaccines and cost of administration, optimization becomes a necessity. Previous approaches using mathematical programming methods have shown to be effective but are limited by computational costs. In this work, we present PREEMPT, a new approach for intervention via maximizing the influence of vaccinated nodes on the network.We prove submodular properties associated with the objective function of our method so that it aids in construction of an efficient greedy approximation strategy. Consequently, we present a new parallel algorithm based on greedy hill climbing for PREEMPT, and present an efficient parallel implementation for distributed CPU-GPU heterogeneous platforms. Our results demonstrate that PREEMPT is able to achieve a significant reduction (up to 6:75) in the percentage of people infected and up to 98% reduction in the peak of the infection on a cityscale network. We also show strong scaling results of PREEMPT on up to 128 nodes of the Summit supercomputer. Our parallel implementation is able to significantly reduce time to solution, from hours to minutes on large networks. This work represents a first-of-its-kind effort in parallelizing greedy hill climbing and applying it toward devising effective interventions for epidemics.
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- PAR ID:
- 10213737
- Date Published:
- Journal Name:
- International Conference for High Performance Computing Networking Storage and Analysis
- ISSN:
- 2167-4337
- Page Range / eLocation ID:
- 765-799
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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