Demand Side Management, DSM, is a program supported by the smart-grid which aims at matching the energy consumption and production. Several techniques for demand-side management have been proposed, including load-shed...
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A parallel LC-link PV inverter is presented for grid-tied and grid-independent operation. This topology is a single-stage system. This system produces desired sinusoidal voltage and current in autonomous and grid conn...
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ISBN:
(纸本)9789811502149;9789811502132
A parallel LC-link PV inverter is presented for grid-tied and grid-independent operation. This topology is a single-stage system. This system produces desired sinusoidal voltage and current in autonomous and grid connection operation. In this topology inductor and capacitor are connected in parallel between unidirectional switches and bidirectional switches. The inductor is an energy storage element at the link and it charges and discharges through the unidirectional and bidirectional active switches, respectively. The capacitor connected in across the link inductor and it provides resonance. Hence the active switches are turned-on at zero voltage which reduces the switching losses and voltage effect on the switches. In this work parallel LC-link is used in place of electrolytic capacitor because it is an unreliable component and is very sensitive to temperature. Most of the inverters fail because of electrolytic capacitors, particularly at high temperature. Hence an LC pair is used in this work which replaces an electrolytic capacitor to improve the reliability of this PV inverter. The performance and operation are observed in various modes at high frequency as a result of high frequency operation make inductor and capacitor size compact. The modes are obtained through charging and discharging of the inductor. This type of inverter is highly efficient, reliable with high power density. Simulation is done by considering a 30 kW PV operation in grid interconnection and stand-alone modes. The frequency of link is considered to be 6 kHz. PSCAD/EMTDC v4.6 is used for simulation work.
The proceedings contain 18 papers. The topics discussed include: identifying the end-to-end backbone of information diffusion;honeypot contract risk warning on ethereum smart contracts;GraphLib: a parallel graph minin...
ISBN:
(纸本)9781728169811
The proceedings contain 18 papers. The topics discussed include: identifying the end-to-end backbone of information diffusion;honeypot contract risk warning on ethereum smart contracts;GraphLib: a parallel graph mining library for joint cloud computing;a cost-efficient and low-latency data hosting scheme in JointCloud storage;Jupiter: a modern federated learning platform for regional medical care;communication-efficient collaborative learning of geo-distributed JointCloud from heterogeneous datasets;a distributedcomputing framework based on variance reduction method to accelerate training machine learning models;and cross-domain workloads performance prediction via runtime metrics transferring.
We are motivated by newly proposed methods for data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of res...
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ISBN:
(纸本)9781728199986
We are motivated by newly proposed methods for data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover how concepts relate to one another. They construct graph representations from annotated text databases and then formulate the relationship -mining problem as one of computing all -pairs shortest paths (APSP), which becomes a significant bottleneck. In this context, we present a new high-performance algorithm and implementation of the FloydWarshall algorithm for distributed -memory parallel computers accelerated by GPUs, which we call DSNAPSHOT (distributed Accelerated Semiring All -Pairs Shortest Path). For our largest experiments, we ran DSNAPSHOT on a connected input graph with millions of vertices using 4,096 nodes (24,576 GPUs) of the Oak Ridge National Laboratory's Summit supercomputer system. We find DSNAPSHOT achieves a sustained performance of 136 x 1015 floating-point operations per second (136 petaflop/s) at a parallel efficiency of 90% under weak scaling and, in absolute speed, 70 % of the best possible performance given our computation (in the single -precision tropical semiring or "min -plus" algebra). Looking forward, we believe this novel capability will enable the mining of scholarly knowledge corpora when embedded and integrated into artificial intelligence -driven natural language processing workflows at scale.
Correlated electronic structure calculations enable an accurate prediction of the physicochemical properties of complex molecular systems; however, the scale of these calculations is limited by their extremely high co...
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ISBN:
(纸本)9781665454452
Correlated electronic structure calculations enable an accurate prediction of the physicochemical properties of complex molecular systems; however, the scale of these calculations is limited by their extremely high computational cost. The Fragment Molecular Orbital (FMO) method is arguably one of the most effective ways to lower this computational cost while retaining predictive accuracy. In this paper, a novel distributed many-GPU algorithm and implementation of the FMO method are presented. When applied in tandem with the Hartree-Fock and RI-MP2 methods, the new implementation enables correlated calculations on 623,016 electrons and 146,592 atoms in less than 45 minutes using 99.8% of the Summit supercomputer (27,600 GPUs). The implementation demonstrates remarkable speedups with respect to other current GPU and CPU codes, and excellent strong scalability on Summit achieving 94.6 % parallel efficiency on 4600 nodes. This work makes feasible correlated quantum chemistry calculations on significantly larger molecular systems than before and with higher accuracy.
Graph Convolutional Networks (GCN) have become a popular means of performing link prediction due to the high accuracy offered by them. However, scaling such link prediction into large graphs of billions of vertices an...
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ISBN:
(纸本)9781728187808
Graph Convolutional Networks (GCN) have become a popular means of performing link prediction due to the high accuracy offered by them. However, scaling such link prediction into large graphs of billions of vertices and edges with rich types of attributes is a significant issue to be addressed due to the storage and computation limitations of the machines. In this paper we present a scalable link prediction approach which conducts GCN training and link prediction on top of a distributed graph database server called JasmineGraph. We partition graph data and persist them in multiple workers. We implement parallel graph node embedding generation using GraphSAGE algorithm in multiple workers. Our approach avoids facing performance bottlenecks in GCN training using an intelligent scheduling algorithm. We show our approach scales well with an increasing number of partitions (2,4,8, and 16) using four real world data sets called Twitter, Amazon, Reddit, and DBLP-V11. JasmineGraph was able to train a GCN from the largest dataset DBLP-V11 (> 9.3GB) in 11 hours and 40 minutes time using 16 workers on a single server while the original GraphSAGE implementation could not process it at all. The original GraphSAGE implementation processed the second largest dataset Reddit in 238 minutes while JasmineGraph took only 100 minutes on the same hardware with 16 workers leading to 2.4 times improved performance.
Knowledge Graph (KG) is currently the most popular graph structure. For graphs, however, they are frequently incomplete due to the lack of most information in the real world. Knowledge Graph Completion (KGC) has becom...
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Knowledge Graph (KG) is currently the most popular graph structure. For graphs, however, they are frequently incomplete due to the lack of most information in the real world. Knowledge Graph Completion (KGC) has become the most interesting topic for researchers. At the same time, the relationships in social networks have expanded from binary relationships to more complex high-order relationships. However, the current research methods on multi-relationship still lack the interaction between entities, and the corresponding entity information under each relationship is different. Based on this, we propose a high-order GCN-based multi-relation prediction model (denoted as MHGCN). Firstly, an entity adjacent matrix is constructed for each relation, and a high-order graph convolutional network (GCN) is used to propagate the neighbor information among entities within the relation. Secondly, a probabilistic calculation method that integrates entity information is designed to judge the existence of facts. Experimental analysis is carried out on three representative datasets, which illustrate the effectiveness of our proposed algorithm. In particular, for FB-AUTO dataset with unfixed number of entities, the MRR of MHGCN reaches 0.883. The MRR of the fixed entity number dataset JF17K-4 is up to 0.828. It further shows that MHGCN is not only applicable to datasets with unfixed number of entities, but also applicable to datasets with fixed number of entities.
Unsupervised text summarization is a promising approach that avoids human efforts in generating reference summaries, which is particularly important for large-scale datasets. To improve its performance, we propose a h...
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Computer architectures that presume global hardware determinism are ultimately unscalable, but they are relatively easy to program because each operation is strictly sequenced and has an assured effect. Architectures ...
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It is a key issue to efficiently manage resources in the smart grid (SG) network that is a dynamic distributedgrid, in which the production, storage and users of electricity will work together under specific control....
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