Search

Studies employing neural networks to classify emotions from brain waves and other biological signals provide a quantitative perspective on understanding human physiological phenomena. Typically, multimodal networks process combined data without considering the relationships between electrodes, such as in electroencephalograms (EEG) where data are gathered from multiple inputs. However, incorporating electrode relationships when combining data may improve the model accuracy. This study explores EEG data, often treated as a single modality, input into networks of varied structures as a multimodal data stream to evaluate accuracy. Additionally, it investigates the effect of input electrode combination patterns on accuracy. The results underscore the importance of designing appropriate electrode models when integrating EEG data into networks with various structures. Under the conditions of this study, the highest accuracy of 89.08% was obtained by the most appropriate model, significantly surpassing others. Therefore, when incorporating multi-channel EEG data into neural networks, the structure of the model’s specific section receiving the EEG signal must be considered.

High-performance computing (HPC) cluster systems sometimes adopt a two-layered file system composed of local and global file systems to achieve both capacity and performance in storage. In such a cluster system, the input data of an application needs to be staged from the global storage into the local storage, and the output data needs to be staged from the local storage out to the global storage. This staging operation must be efficiently and quickly performed to gain higher job throughput because an inefficient staging operation prevents waiting job requests from being executed. In particular, in the case of the cluster system with the oversubscribed interconnect shared by the storage and the computing nodes, the inter-node communication and this staging operation traffic collides, which may degrade the job throughput. In this research, we focus on the traffic collision of the inter-node communication and the staging traffic to improve job throughput, targeting the cluster system with the oversubscribed interconnect where these two types of traffic flow. In other words, whether the dynamic control of the traffic flow derived from the staging operation leads to the improvement in the job throughput or not is investigated. For the investigation, we present a traffic collision avoidance method to dynamically configure a set of data paths for each type of the traffic only while the staging operation is conducted. The evaluation in this article shows that the proposed method avoids a traffic collision and accelerates the staging operation by 22.0% on our cluster system. Also, this evaluation indicates the overhead of the application incurred by the proposed method is negligible. Furthermore, 8.7% of the job execution time is reduced by the proposed method.

Message passing interface (MPI) communication performance is becoming one of the key factors heavily affecting the total performance of data-intensive applications running on computer clusters. Our software-defined networking (SDN)-enhanced MPI improves the performance of communication over interconnects by integrating flexible and dynamic network controllability of SDN into MPI. We have demonstrated that the acceleration of individual MPI communication primitives is feasible through our past work on the SDN-enhanced MPI. However, real-world MPI applications have not benefited from such accelerated communication primitives through our research achievements to date, because each of the distinct network control algorithms designed for various MPI communication primitives cannot be activated and coordinated with the execution of the MPI application. Therefore, this paper proposes UnisonFlow, a software-defined coordination mechanism for the SDN-enhanced MPI that performs network control in synchronization with the execution of applications. An experiment conducted on a real-computer cluster verifies that the interconnect control can be successfully performed in synchronization with the execution of the application. Furthermore, the synchronization is performed with a low overhead and its performance penalty is practically negligible.

Combining the concept of software control with existing legacy network technology promotes new networking services with low capital expenditure. Currently, many testbeds and academic operators provide bandwidth on demand (BoD) services over layers 2 and 3 wide area network. BoD decreases operational expenditures and manual errors. However, administrators must continue to manually configure virtual circuits for different stakeholders to accommodate configuration conflicts, quality of service (QoS), and service policies. This paper proposes a BoD framework called AutoNET to provide a set of BoD services with independent abstract topologies, resource allocation, and different QoS levels. User authorization is employed to control the eligibility to access a given BoD service. AutoNET uses the extended On-Demand Secure Circuits and Advance Reservation System (OSCARS) as controller software and MultiProtocol Label Switching (MPLS) as a transport technology.