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Register a new userA Timer-Augmented Cost Function for Load Balanced DSMC
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Added by
Paolo Bientinesi
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Authors
William McDoniel
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Due to a hard dependency between time steps, large-scale simulations of gas using the Direct Simulation Monte Carlo (DSMC) method proceed at the pace of the slowest processor. Scalability is therefore achievable only by ensuring that the work done each time step is as evenly apportioned among the processors as possible. Furthermore, as the simulated system evolves, the load shifts, and thus this load-balancing typically needs to be performed multiple times over the course of a simulation. Common methods generally use either crude performance models or processor-level timers. We combine both to create a timer-augmented cost function which both converges quickly and yields well-balanced processor decompositions. When compared to a particle-based performance model alone, our method achieves 2x speedup at steady-state on up to 1024 processors for a test case consisting of a Mach 9 argon jet impacting a solid wall.
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Fat-tree networks have been widely adopted to High Performance Computing (HPC) clusters and to Data Center Networks (DCN). These parallel systems usually have a large number of servers and hosts, which generate large volumes of highly-volatile traffic. Thus, distributed load-balancing routing design becomes critical to achieve high bandwidth utilization, and low-latency packet delivery. Existing distributed designs rely on remote congestion feedbacks to address congestion, which add overheads to collect and react to network-wide congestion information. In contrast, we propose a simple but effective load-balancing scheme, called Dynamic Randomized load-Balancing (DRB), to achieve network-wide low levels of path collisions through local-link adjustment which is free of communications and cooperations between switches. First, we use D-mod-k path selection scheme to allocate default paths to all source-destination (S-D) pairs in a fat-tree network, guaranteeing low levels of path collision over downlinks for any set of active S-D pairs. Then, we propose Threshold-based Two-Choice (TTC) randomized technique to balance uplink traffic through local uplink adjustment at each switch. We theoretically show that the proposed TTC for the uplink-load balancing in a fat-tree network have a similar performance as the two-choice technique in the area of randomized load balancing. Simulation results show that DRB with TTC technique achieves a significant improvement over many randomized routing schemes for fat-tree networks.
We present a novel framework, called balanced overlay networks (BON), that provides scalable, decentralized load balancing for distributed computing using large-scale pools of heterogeneous computers. Fundamentally, BON encodes the information about each nodes available computational resources in the structure of the links connecting the nodes in the network. This distributed encoding is self-organized, with each node managing its in-degree and local connectivity via random-walk sampling. Assignment of incoming jobs to nodes with the most free resources is also accomplished by sampling the nodes via short random walks. Extensive simulations show that the resulting highly dynamic and self-organized graph structure can efficiently balance computational load throughout large-scale networks. These simulations cover a wide spectrum of cases, including significant heterogeneity in available computing resources and high burstiness in incoming load. We provide analytical results that prove BONs scalability for truly large-scale networks: in particular we show that under certain ideal conditions, the network structure converges to Erdos-Renyi (ER) random graphs; our simulation results, however, show that the algorithm does much better, and the structures seem to approach the ideal case of d-regular random graphs. We also make a connection between highly-loaded BONs and the well-known ball-bin randomized load balancing framework.
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Accurate load prediction is an effective way to reduce power system operation costs. Traditionally, the mean square error (MSE) is a common-used loss function to guide the training of an accurate load forecasting model. However, the MSE loss function is unable to precisely reflect the real costs associated with forecasting errors because the cost caused by forecasting errors in the real power system is probably neither symmetric nor quadratic. To tackle this issue, this paper proposes a generalized cost-oriented load forecasting framework. Specifically, how to obtain a differentiable loss function that reflects real cost and how to integrate the loss function with regression models are studied. The economy and effectiveness of the proposed load forecasting method are verified by the case studies of an optimal dispatch problem that is built on the IEEE 30-bus system and the open load dataset from the Global Energy Forecasting Competition 2012 (GEFCom2012).
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