Integral System-Level Source Terms and their Consequence
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Proceedings - IEEE International Conference on Cluster Computing, ICCC
Avoiding communication bottlenecks remains a critical challenge in high-performance computing (HPC) as systems grow to exascale. Numerous design possibilities exist for avoiding network congestion including topology, adaptive routing, congestion control, and quality-of-service (QoS). While network design often focuses on topological features like diameter, bisection bandwidth, and routing, efficient QoS implementations will be critical for next-generation interconnects. HPC workloads are dominated by tightly-coupled mathematics, making delays in a single message manifest as delays across an entire parallel job. QoS can spread traffic onto different virtual lanes (VLs), lowering the impact of network hotspots by providing priorities or bandwidth guarantees that prevent starvation of critical traffic. Two leading topology candidates, Dragonfly and Fat Tree, are often discussed in terms of routing properties and cost, but the topology can have a major impact on QoS. While Dragonfly has attractive routing flexibility and cost relative to Fat Tree, the extra routing complexity requires several VLs to avoid deadlock. Here we discuss the special challenges of Dragonfly, proposing configurations that use different routing algorithms for different service levels (SLs) to limit VL requirements. We provide simulated results showing how each QoS strategy performs on different classes of application and different workload mixes. Despite Dragonfly's desirable characteristics for adaptive routing, Fat Tree is shown to be an attractive option when QoS is considered.
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Proceedings - IEEE International Conference on Cluster Computing, ICCC
Avoiding communication bottlenecks remains a critical challenge in high-performance computing (HPC) as systems grow to exascale. Numerous design possibilities exist for avoiding network congestion including topology, adaptive routing, congestion control, and quality-of-service (QoS). While network design often focuses on topological features like diameter, bisection bandwidth, and routing, efficient QoS implementations will be critical for next-generation interconnects. HPC workloads are dominated by tightly-coupled mathematics, making delays in a single message manifest as delays across an entire parallel job. QoS can spread traffic onto different virtual lanes (VLs), lowering the impact of network hotspots by providing priorities or bandwidth guarantees that prevent starvation of critical traffic. Two leading topology candidates, Dragonfly and Fat Tree, are often discussed in terms of routing properties and cost, but the topology can have a major impact on QoS. While Dragonfly has attractive routing flexibility and cost relative to Fat Tree, the extra routing complexity requires several VLs to avoid deadlock. Here we discuss the special challenges of Dragonfly, proposing configurations that use different routing algorithms for different service levels (SLs) to limit VL requirements. We provide simulated results showing how each QoS strategy performs on different classes of application and different workload mixes. Despite Dragonfly's desirable characteristics for adaptive routing, Fat Tree is shown to be an attractive option when QoS is considered.
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Proceedings - IEEE International Conference on Cluster Computing, ICCC
We introduce a new HPC system high-speed network fabric production monitoring tool, the ibnet sampler plugin for LDMS version 4. Large-scale testing of this tool is our work in progress. When deployed appropriately, the ibnet sampler plugin can provide extensive counter data, at frequencies up to 1 Hz. This allows the LDMS monitoring system to be useful for tracking the impact of new network features on production systems. We present preliminary results concerning reliability, performance impact, and usability of the sampler.
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Recent developments at Sandia in meshfree methods have delivered improved robustness in solid mechanics problems that prove difficult for traditional Lagrangian, mesh-based finite elements. Nevertheless, there remains a limitation in accurately predicting very large material deformations. It seems robust meshfree discretizations and integration schemes are necessary, but not sufficient, to close this capability gap. This state of affairs directly impacts current and future LEPs, whose simulation needs are not well met for extremely large deformation problems. We propose to use a new numerical framework, the Optimal Transportation Meshfree (OTM) method enhanced by meshfree adaptivity, as we believe that a combination of both will provide a novel way to close this capability gap.
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