The 24th Workshop on Advances in Parallel and Distributed Computational Models (APDCM), which was held in conjunction with the International Parallel and Distributed Processing Symposium (IPDPS) on May 30 - June 3, 2022, aims to provide a timely forum for the exchange and dissemination of new ideas, techniques and research in the field of the parallel and distributed computational models. The APDCM workshop has a history of attracting participation from reputed researchers worldwide. The program committee has encouraged the authors of accepted papers to submit full-versions of their manuscripts to the International Journal of Networking and Computing (IJNC) after the workshop. After a thorough reviewing process, with extensive discussions, four articles on various topics have been selected for publication on the IJNC special issue on APDCM. On behalf of the APDCM workshop, we would like to express our appreciation for the large efforts of reviewers who reviewed papers submitted to the special issue. Likewise, we thank all the authors for submitting their excellent manuscripts to this special issue. We also express our sincere thanks to the editorial board of the International Journal of Networking and Computing, in particular, to the Editor-in-chief Professor Koji Nakano. This special issue would not have been possible without his support.

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Quantum annealing (QA) has shown good solutions quickly in benchmarks of combinatorial optimization problems. There are not many real-world problems that illustrate the effectiveness of QA compared to the typical optimization methods. We applied QA to a shift scheduling problem for call centers with four conditions. The shift scheduling problem for call centers optimizes shifts for a specific period of time while satisfying various constraints, such as satisfying the desired worker's shifts. In this paper, we formulated the shift scheduling problem for call centers in QUBO to apply QA. We tuned various parameters required to execute the QA. Then, we compared QA with six typical optimization methods using a classical computer to investigate QA's effectiveness for the shift scheduling problem for call centers. The results show that for three instances with up to 126 variables, QA is about 3-14 times faster than other methods and obtains enough quality of solutions. This shows that QA can be one of the effective methods to solve practical applications in the real-world.

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Motivated by the manipulation of nanoscale materials, recent investigations have focused on hybrid systems where passive elements incapable of movement, called tiles, are manipulated by one or more mobile entities, called robots, with limited computational capabilities. Like in most self-organizing systems, the fundamental concern is with the (geometric) shapes created by the position of the tiles; among them, the line is perhaps the most important. The existing investigations have focused on formation of the shape, but not on its reconfiguration following the failure of some of the tiles. In this paper, we study the problem of maintaining a line formation in presence of dynamic failures: any tile can stop functioning at any time. We show how this problem can be solved by a group of very simple robots, with the computational power of deterministic finite automata.

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The application of approximate computing (AC) in optimizing tradeoffs among performance, power consumption, and accuracy of computation results can be improved by adjusting data precision in applications. The importance of AC has increased over the years as it is used to maximize performance even with limited power budget and hardware resources in high performance computing (HPC) systems that require more precise computations. To apply AC for HPC applications effectively, we must consider the character of each message passing interface (MPI) rank in an application and optimize it by adjusting its data precision. This rank-level AC ensures that ranks and threads in an application run with data precision and perform data transfer while converting the precision of target data. In this paper, we have proposed and evaluated data pack/unpack application programming interfaces (APIs), which are applicable for standard MPI programs run on HPC systems, for converting the precision of target data. The proposed APIs enable us to express data transfer among ranks with different precisions. In addition, we have also developed a reasonable performance model to select an appropriate data transfer API for maximizing performance with rank-level AC based on performance evaluation with various HPC systems.

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To amortize the cost of MPI communications, distributed parallel HPC applications can overlap network communications with computations in the hope that it improves global application performance. When using this technique, both computations and communications are running at the same time. But computation usually also performs some data movements. Since data for computations and for communications use the same memory system, memory contention may occur when computations are memory-bound and large messages are transmitted through the network at the same time. In this paper we propose a model to predict memory bandwidth for computations and for communications when they are executed side by side, according to data locality and taking contention into account. Elaboration of the model allowed to better understand locations of bottleneck in the memory system and what are the strategies of the memory system in case of contention. The model was evaluated on many platforms with different characteristics, and showed a prediction error in average lower than 4%.

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