International Symposium on Computing and Networking (<scp>CANDAR</scp> 2019) special issue

The past years have seen a flurry of activity in the area of distributed computing and its related fields. Indeed, developments in computer communications and networks enabled the deployment of exciting new areas, including internet of things, vehicular networks, collaborative big data analysis, and so on. The design and implementation of energy efficient future generation communication and networking technologies fostered the development of mobile, pervasive, and large-scale computing technologies. The recent growth of high speed wired/wireless access and LANs, functional wireless terminals such as smart phones and tablets, and cost-effective sensor/tag devices increasingly stimulate the emergence of user-centric network services collecting and exploiting user-provided data. Likewise, general purpose computing on graphics processing unit (GPGPU) has superseded high-performance CPU in several important tasks, including computer graphics, physics calculations, encryption/decryption, and scientific computations. Indeed, GPGPUs are seen as a natural alternative to fulfill the computing needs for artificial intelligence and machine learning applications. Recent developments have shown intensive research activity in these fields. Novel parallel and distributed computational models reflected on the advances in new computational devices and environments such as optical interconnects, programmable logic arrays, networks of workstations, radio communications, mobile computing, DNA computing, quantum computing, sensor networks, and so forth. It is very encouraging to note that the advent of these new models has led to significant advances in the resolution of various difficult problems of practical interest. The International Symposium on Computing and Networking (CANDAR 2019), in its eighth edition, served as a forum for exchanging the latest findings and experiences ranging from theoretical research to practical system development in all aspects of computing and networking. The symposium is meant to bring together researchers and practitioners to share their views in an open and mutually beneficial exchanges of ideas among the participants. This special issue brings selected papers presented at the CANDAR2019 Symposium and its workshops. The first contribution, entitled “Efficient Parallel Implementations to Compute the Diameter of a Graph” by Takafuji et al., presents an efficient implementations of the Blocked Floyd-Warshall algorithm, which uses no barrier synchronization and invokes only one kernel call by using Single Kernel Soft Synchronization (SKSS) techniques. Experimental results using NVIDIA Tesla V100 show that the proposed implementation is 1.05–1.31 times faster than the previously published articles. Also, the authors proposed an efficient GPU implementation to execute the Blocked Floyd-Warshall algorithm for many graphs at the same time. The next contribution is given by Plauth et al., entitled “Improved Data Transfer Efficiency for Scale-Out GPU Workloads using On-the-Fly I/O Link Compression”. The paper evaluates the potential benefits of 842-based On-the-Fly I/O Link Compression in a scale-out scenario using matrix multiplication, a database query, and a text search kernel as common, data-intensive workloads. By augmenting the CloudCL framework with 842-based compression facilities, the paper demonstrates that transparent On-the-Fly I/O Link Compression can yield performance improvements on tested scale-out GPU workloads. In a different context, Farias et al. considered broadcast problem in vehicular networks. More precisely, the paper aims at reducing the rate of periodic messages (i.e., beacons) that report vehicular information required by safety applications. The proposed neighbor congestion avoidance protocol (NCAP) employs a proactive strategy to predict vehicle position using Kalman filter and an efficient event-driven message delivery mechanism. Compared to similar strategies, the authors show that NCAP reduced the number of beacons and event-driven message retransmissions significantly. Bénassy et al. submitted the paper entitled “Eventually Consistent Distributed Ledger Despite Degraded Atomic Broadcast”. The authors denote that distributed ledger or blockchain technologies have been widespread in recent years. This, in turn, motivates malicious users to attempt to break the system or take advantage of it. The paper focus on attacks that may damage underlying networks of distributed ledgers. Underlying networks offer useful communication primitives such as an atomic broadcast. However, such attacks may degrade the property of the primitives turning distributed ledgers relying on the primitives to no longer work. The authors proposed algorithms to make the distributed ledgers that still work even when some attacks degrade the primitives. The next contribution is given by Yasudo et al., entitled “Designing Low-Diameter Interconnection Networks with Multi-ported Host-Switch Graphs”. A host-switch graph represents a network topology of a computer systems with 1-port host computers and ∆-port switches. A host computer is usually connected to multiple switches using InfiniBand, NVSwitch, or Omni-Path to provide high bandwidths. However, as host-switch graph cannot represent such systems, the authors investigate interconnection networks with multi-port hosts by introducing a multi-ported host-switch graph. The paper presents method for minimizing the diameter of switch components by leveraging multi-port hosts. The method builds on the concept that one should maximize the number of switch components with the minimum diameter. The paper shows that the diameter minimization is equivalent to solving the degree diameter problem for bipartite graphs of diameter three. Experimental results show that diameter can be drastically reduced as well as improving bandwidth. The contribution by Matsumura et al., entitled “A Novel Structured Sparse Fully-Connected Layer in Convolutional Neural Networks”, is motivated by the fact that convolutional neural networks (CNNs) has been one of the drivers to support the rapid development of artificial intelligent techniques. However, as the ability of the network increases, the size of the network becomes larger. Hence, reduction of the network size has been presented in the literature. In many cases, the proposed approaches produce an unstructured network which prevents efficient parallel computation. To avoid this problem, the paper presents a novel structured sparse Fully-Connected Layer (FCL) for CNNs. The proposed approach reduces the number of network parameters in the FCLs which occupy a large part of network parameters. Unlike the general FCLs used in the popular CNNs such as AlexNet and VGG-16, the proposed approach reduces the connection between the last convolutional layer and the first FCL. In addition, the paper proposes an efficient implementation for the proposed sparse FCLs on the GPU. The authors evaluated the proposed approach for AlexNet and VGG-16 on ILSVRC-2012 dataset and various small datasets. The contribution by Ashkenazi et al., entitled “Forgive & Forget: Self-Stabilizing Swarms in Spite of Byzantine Robots”, considers the case in which a swarm of robots collaborates in a mission, where a few of the robots behave maliciously. These malicious Byzantine robots may be temporally or constantly controlled by an adversary. The scope is synchronized full information robot operations, where a robot that does not follow the program/policy of the swarm is immediately identified and can be remembered as Byzantine. As robots may be suspected of being Byzantine due to benign temporal malfunctions, it is imperative to forgive and forget, otherwise, a robot cannot assume collaborative actions with any other robot in the swarm. Still, remembering for a while may facilitate a policy of surrounding, isolating, and freezing the movement of the misbehaving robots, by several robots, allowing the rest to perform the swarm task with no intervention. The authors demonstrate the need to periodically forgive and forget to realize swarm several tasks including patrolling/cleaning in the presence of possible Byzantine robots. The policy for achieving the task consists of blocking the movement of the Byzantine robot(s) by some of the robots, while the rest patrol/clean the plane. We would like to express sincere gratitude to all the authors who submitted their valuable contributions to this special issue, as well as to all the experts who participated in the review process. We are immensely grateful to the Editor-in-Chief Prof. David Walker and the former Editor-in-Chief Prof. Geoffrey C. Fox for providing this opportunity to contribute to the Concurrency and Computation: Practice and Expertise journal as well for their invaluable support and guidance during the publication process.