Novel parallel processing techniques for IoT ‐based machine learning applications
Cristiana Barbosa Bentes, Felipe M. G. França, Leandro A. J. Marzulo, Gabriele Mencagli, Mauricio Lima Pilla · Concurrency and Computation Practice and Experience · 2021
This special issue of Concurrency and Computation: Practice and Experience comprises four papers extending the original workshop publications accepted and presented at MPP 2019 (8th Workshop on Parallel Programming Models – Special Issue on IoT and Machine Learning), held in Rio de Janeiro in conjunction with IPDPS 2019. The papers represent interesting research ideas of parallel programming models, tools, and optimizations suited for being applied to IoT-based applications. The paper titled “Enabling Heterogeneous Ray-Tracing Acceleration in Edge/Cloud Architectures” represents a very interesting research on reconfigurable accelerators for Ray-Tracing, specialized in computing ray-triangle intersections at the network edge of a heterogeneous cloud computing environment. The authors validated their approach on the Xilinx Zynq FPGA platform. The paper titled “An Incremental Reinforcement Learning Scheduling Strategy for Data-Intensive Scientific Workflows in the Cloud” is a research work proposing a new scheduling algorithm based on Reinforcement Learning for scientific workflows on HPC distributed resources and Clouds. The paper titled “Latency-aware Adaptive Micro-Batching Techniques for Streamed Data Compression on GPUs” proposes a set of Autonomic Computing strategies for configuring the optimal parallelism degree and batch size on streaming data compression parallel applications on GPU architectures. Finally, the paper titled “Gamma – General Abstract Model for Multiset mAnipulation and Dynamic Dataflow Model: an Equivalence Study” is an interesting research study on new applications of the Gamma Model (General Abstract Model for Multiset mAnipulation) and its joint utilization with the Dataflow programming model. As Guest Editors, we would like to express our gratitude for the valuable contributions made by all authors. We would like to thank all the reviewers who helped us during the thorough review process based on several review rounds. Finally, we would like to thank all the editorial board members and all staff of Concurrency and Computation: Practice and Experience for allowing us to publish our Special Issue and for their invaluable support during the whole publication process.