The proceedings contain 205 papers. The topics discussed include: estimator for stochastic channel model without multipath extraction using temporal moments;least squares phase estimation of 1-bit quantized signals wi...
ISBN:
(纸本)9781538665282
The proceedings contain 205 papers. The topics discussed include: estimator for stochastic channel model without multipath extraction using temporal moments;least squares phase estimation of 1-bit quantized signals with phase dithering;energy minimization of mobile edge computing networks with finite retransmissions in the finite blocklength regime;RLS precoding for massive MIMO systems with nonlinear front-end;wireless map-reduce distributed computing with full-duplex radios and imperfect CSI;low complexity synchronization for offset tolerant DFT-based BFSK demodulator;on ultra-reliable and low latency simultaneous information and energy transmission systems;the potential of continuous phase modulation for oversampled 1-bit quantized channels;sparse bayesian estimation of millimeter-wave channel correlation matrix;and towards hardware implementation of neural network-based communication algorithms.
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. The technological trends in HPC system evolution indicates an increasing bur...
详细信息
ISBN:
(数字)9781728174457
ISBN:
(纸本)9781728174570
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. The technological trends in HPC system evolution indicates an increasing burden placed on application developers due to the management of the unprecedented complexity levels of hardware and its associated performance characteristics. Many existing scientific applications codes are unlikely to perform well on future systems without major modifications or even complete rewrites. In the future, it will be necessary to utilize, in concert, many characteristics such as multiple levels of parallelism, many lightweight cores, complex memory hierarchies, novel I/O technology, power capping, system-wide temporal/spatial performance heterogeneity and reliability concerns. The parallel and distributed computing (PDC) community has developed new programming models, algorithms, libraries and tools to meet these challenges in order to accommodate productive code development and effective system use. However, the scientific application community still needs to identify the benefit through practical evaluations. Thus, the focus of this workshop is on methodologies and experiences used in scientific and engineering applications and algorithms to achieve sustainable code development for better productivity, application performance and reliability.
In many domains, Discrete-Event Simulations (DES) are usually used to reproduce the behavior of a certain system or process, where events are processed one after another in chronological and sequential order. Classica...
详细信息
ISBN:
(纸本)9781728106762
In many domains, Discrete-Event Simulations (DES) are usually used to reproduce the behavior of a certain system or process, where events are processed one after another in chronological and sequential order. Classical DES will no longer be a possible solution for Complex and Large-scale systems, System of systems (SoS), and Performance Evaluation systems that compare multiple different simulations running simultaneously in parallel. advances in network and communications made the distributed Simulation (DS) approach one of the best solutions for the aforementioned systems Simulations. One of the challenges faced when developing a DS from DES components is the federation behavior including time management and synchronization between these components. In most of the traditional DES platforms, simulations cannot exchange messages, nor change the configuration at run time. This makes the DES connection and integration very hard and at times, impossible to implement. This article presents the method used to integrate different DES components, using High-Level Architecture (HLA) Evolved Standard, Business Process Model and Notation (BPMN), and Jaamsim, a Java open source DES.
High-availability softwarized networks, while offering tremendous flexibility, typically require distributed implementations to ensure resilience and scalability. For example, in software-defined networking (SDN), the...
详细信息
ISBN:
(纸本)9781538693766
High-availability softwarized networks, while offering tremendous flexibility, typically require distributed implementations to ensure resilience and scalability. For example, in software-defined networking (SDN), the control plane constitutes the "network brain," and is implemented in a distributed fashion to avoid a single point of failure, while sophisticated algorithms are used to ensure that the distributed controller instances operate together as a logically centralized entity. distributed consensus algorithms such as Raft are used in leading open source distributed SDN controller implementations, such as ONOS and ODL, to guarantee strong consistency of critical replicated data and provide resiliency under failures. We demonstrate the vulnerability of SDN distributed controller software (ONOS) to host mobility-based DDoS attacks, and show that (bursty) DDoS attacks and (intermittent) overload in network demands trigger a form of software "babble" that causes SDN to violate key assumptions of Raft, resulting in significant unavailability of critical communication between the control plane and data plane switches due to Raft behaviors. We propose BabbleResistantRaft, a "babble-resistant" variant of Raft that ensures safety, liveness, and stability under these types of attacks and network conditions, and demonstrate the effectiveness of BabbleResistantRaft through our implementation extending the open-source pysyncobj Raft library.
Recent advances in multi-agent reinforcement learning show that agents can spontaneously learn when and what to communicate with each other to support effective cooperation. However, the existing approaches assume a f...
详细信息
ISBN:
(数字)9781728114422
ISBN:
(纸本)9781728114422
Recent advances in multi-agent reinforcement learning show that agents can spontaneously learn when and what to communicate with each other to support effective cooperation. However, the existing approaches assume a fully-connected network with unlimited bandwidth, which is impractical in many real-world scenarios. For instance, in many multi-robot applications, robots are connected only through an unstable wireless network with limited bandwidth. Therefore, we must enable the agents to learn communication strategy that takes the consumption of network resources into account. This paper proposes a group division-based attentional communication model (GDAC), which can divide agents into groups according to their "attention" in the learned communication strategy. According to the novel "attention" mechanism, agents can be dynamically grouped according to their task relevance, and the communication only takes places inside the same group. As a result, it avoids a fully-connected communication architecture and can significantly reduce the bandwidth consumption at runtime. This model has been successfully applied to the environmental exploration task with a group of agents. The results show that GDAC could effectively reduce the total amount of communication message and yield improved performance over the existing fully-connected communication architecture.
The proceedings contain 8 papers. The topics discussed include: scalable hyperparameter optimization with lazy Gaussian processes;understanding scalability and fine-grain parallelism of synchronous data parallel train...
ISBN:
(纸本)9781728159850
The proceedings contain 8 papers. The topics discussed include: scalable hyperparameter optimization with lazy Gaussian processes;understanding scalability and fine-grain parallelism of synchronous data parallel training;DisCo: physics-based unsupervised discovery of coherent structures in spatiotemporal systems;GradVis: visualization and second order analysis of optimization surfaces during the training of deep neural networks;metaoptimization on a distributed system for deep reinforcement learning;scheduling optimization of parallel linear algebra algorithms using supervised learning;parallel data-local training for optimizing Word2Vec embeddings for word and graph embeddings;and fine-grained exploitation of mixed precision for faster CNN training.
With the rapid development of the Internet and big data technologies, high-dimensional data generated in various fields has increased dramatically. Feature selection is an effective way to solve data processing proble...
详细信息
ISBN:
(纸本)9781728140698
With the rapid development of the Internet and big data technologies, high-dimensional data generated in various fields has increased dramatically. Feature selection is an effective way to solve data processing problems caused by high dimensionality and high computational complexity. The traditional feature selection method shows the problem of insufficient classification accuracy and low processing efficiency when dealing with high-dimensional and large-scale data. The traditional feature selection method shows low classification accuracy and low processing efficiency when dealing with high-dimensional and large-scale data. This paper proposed a feature selection method based on Whale Optimization Algorithm to learn mining feature selection rules, then improve the accuracy of feature selection. However, when the data size is very large, the efficiency of single node execution is low. Therefore, this paper combined the Whale Optimization Algorithm with the parallel computing model of the Spark platform, and proposed a feature selection method based on the Spark platform for distributed Whale Optimization Algorithm. The results showed that the excellent result search ability of the Whale Optimization Algorithm combined with the distributed and efficient calculation speed can realize the efficient solution of the feature selection optimization model.
Softwarization enables tremendous flexibility for networks as the use of software-defined networking (SDN) and programmable data planes (e.g. P4) together support dynamic reconfiguration of networks in real-time, in r...
详细信息
ISBN:
(纸本)9781538693766
Softwarization enables tremendous flexibility for networks as the use of software-defined networking (SDN) and programmable data planes (e.g. P4) together support dynamic reconfiguration of networks in real-time, in response to network conditions and new service requests. In such networks, it is imperative to ensure that end-to-end network services continue to satisfy SLAs, especially performance requirements. However, in the presence of unpredictable dynamics due to network reconfigurations, it is not possible to guarantee prior to deployment that a service will meet such SLAs. Run-time verification - in combination with programmable control and data plane monitoring - can provide a basis for detecting potential performance SLA violations, together with identifying and executing appropriate network mitigations. In this paper, we propose a verification transverse based on formal specifications, that spans performance SLAs across the distributed SDN control and programmable data planes, and can coordinate with both planes to execute dynamic reconfiguration that mitigate the detected issues. We demonstrate a proof-of-concept prototype based on an extension of the Aerial run-time verification tool, together with the Inband Network Telemetry (INT) capability, on a network running distributed ONOS controllers together with a P4 data plane.
The feature selection effect directly affects the classification accuracy of the text. This paper introduces a new text feature selection method based on bat optimization. This method uses the traditional feature sele...
详细信息
ISBN:
(纸本)9781728140698
The feature selection effect directly affects the classification accuracy of the text. This paper introduces a new text feature selection method based on bat optimization. This method uses the traditional feature selection method to pre-select the original features, and then uses the bat group algorithm to optimize the pre-selected features in binary code form, and uses the classification accuracy as the individual fitness. However, when the amount of text information is large, the execution time of the single machine is long. According to this shortcoming, combining the Bat Algorithm and the Spark parallel computing framework, the text feature selection algorithm SBATFS is proposed. The algorithm combines the good search performance of the bat algorithm with the distributed and efficient calculation speed to realize the efficient solution of the text feature selection optimization model. The results show that compared with the traditional feature selection method, after SBATFS is used for feature optimization, the classification accuracy is effectively improved.
To efficiently perform collective communications in current high-performance computing systems is a time-consuming task. With future exascale systems, this communication time will be increased further. However, global...
详细信息
ISBN:
(纸本)9781728101767
To efficiently perform collective communications in current high-performance computing systems is a time-consuming task. With future exascale systems, this communication time will be increased further. However, global information is frequently required in various physical models. By exploiting domain knowledge of the model behaviors globally needed information can be distributed more efficiently, using only peer-to-peer communication which spread the information to all processes asynchronous during multiple communication steps. In this article, we introduce a multi-hop based Manhattan Street Network (MSN) for global information exchange and show the conditions under which a local neighbor exchange is sufficient for exchanging distributed information. Besides the MSN, in various models, global information is only needed in a spatially limited region inside the simulation domain. Therefore, a second network is introduced, the local exchange network, to exploit this spatial assumption. Both non-collective global exchange networks are implemented in the massively parallel NAStJA framework. Based on two models, a phase-field model for droplet simulations and the cellular Potts model for biological tissue simulations, we exemplary demonstrate the wide applicability of these networks. Scaling tests of the networks demonstrate a nearly ideal scaling behavior with an efficiency of over 90%. Theoretical prediction of the communication time on future exascale systems shows an enormous advantage of the presented exchange methods of O(1) by exploiting the domain knowledge.
暂无评论