Hpc Big Data And Ai Convergence Towards Exascale

HPC, Big Data, AI Convergence Towards Exascale provides an updated vision on the most advanced computing, storage, and interconnection technologies, that are at basis of convergence among the HPC, Cloud, Big Data, and artificial intelligence (AI) domains. Through the presentation of the solutions devised within recently founded H2020 European projects, this book provides an insight on challenges faced by integrating such technologies and in achieving performance and energy efficiency targets towards the exascale level. Emphasis is given to innovative ways of provisioning and managing resources, as well as monitoring their usage. Industrial and scientific use cases give to the reader practical examples of the needs for a cross-domain convergence. All the chapters in this book pave the road to new generation of technologies, support their development and, in addition, verify them on real-world problems. The readers will find this book useful because it provides an overview of currently available technologies that fit with the concept of unified Cloud-HPC-Big Data-AI applications and presents examples of their actual use in scientific and industrial applications.

This book constitutes the proceedings of the 33rd International Conference on Parallel and Distributed Computing, Euro-Par 2022, held in GLasgow, UK, in August 2022. The 25 full papers presented in this volume were carefully reviewed and selected from 102 submissions. The conference Euro-Par 2022 covers all aspects of parallel and distributed computing, ranging from theory to practice, scaling from the smallest to the largest parallel and distributed systems, from fundamental computational problems and models to full-fledged applications, from architecture and interface design and implementation to tools, infrastructures and applications.

Human Cancer Diagnosis and Detection Using Exascale Computing The book provides an in-depth exploration of how high-performance computing, particularly exascale computing, can be used to revolutionize cancer diagnosis and detection; it also serves as a bridge between the worlds of computational science and clinical oncology. Exascale computing has the potential to increase our ability in terms of computation to develop efficient methods for a better healthcare system. This technology promises to revolutionize cancer diagnosis and detection, ushering in an era of unprecedented precision, speed, and efficiency. The fusion of exascale computing with the field of oncology has the potential to redefine the boundaries of what is possible in the fight against cancer. The book is a comprehensive exploration of this transformative unification of science, medicine, and technology. It delves deeply into the realm of exascale computing and its profound implications for cancer research and patient care. The 18 chapters are authored by experts from diverse fields who have dedicated their careers to pushing the boundaries of what is achievable in the realm of cancer diagnosis and detection. The chapters cover a wide range of topics, from the fundamentals of exascale computing and its application to cancer genomics to the development of advanced imaging techniques and machine learning algorithms. Explored is the integration of data analytics, artificial intelligence, and high-performance computing to move cancer research to the next phase and support the creation of novel medical tools and technology for the detection and diagnosis of cancer. Audience This book has a wide audience from both computer sciences (information technology, computer vision, artificial intelligence, software engineering, applied mathematics) and the medical field (biomedical engineering, bioinformatics, oncology). Researchers, practitioners and students will find this groundbreaking book novel and very useful.

Due to the increasing need to solve complex problems, high-performance computing (HPC) is now one of the most fundamental infrastructures for scientific development in all disciplines, and it has progressed massively in recent years as a result. HPC facilitates the processing of big data, but the tremendous research challenges faced in recent years include: the scalability of computing performance for high velocity, high variety and high volume big data; deep learning with massive-scale datasets; big data programming paradigms on multi-core; GPU and hybrid distributed environments; and unstructured data processing with high-performance computing. This book presents 19 selected papers from the TopHPC2017 congress on Advances in High-Performance Computing and Big Data Analytics in the Exascale era, held in Tehran, Iran, in April 2017. The book is divided into 3 sections: State of the Art and Future Scenarios, Big Data Challenges, and HPC Challenges, and will be of interest to all those whose work involves the processing of Big Data and the use of HPC.

This book constitutes the revised selected papers of the 10th International Conference on Cloud Computing, Big Data & Emerging Topics, JCC-BD&ET 2022, held in La Plata, Argentina*, in June-July 2022. The 9 full papers were carefully reviewed and selected from a total of 23 submissions. The papers are organized in topical sections on: Parallel and Distributed Computing; Machine and Deep Learning; Cloud and High-Performance Computing, Machine and Deep Learning, and Virtual Reality.

This book constitutes the revised selected papers of the 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, held in Oak Ridge, TN, USA*, in August 2020. The 36 full papers and 1 short paper presented were carefully reviewed and selected from a total of 94 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; system software: data infrastructure and life cycle; experimental/observational applications: use cases that drive requirements for AI and HPC convergence; deploying computation: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic.

The two-volume set LNCS 13373 and 13374 constitutes the papers of several workshops which were held in conjunction with the 21st International Conference on Image Analysis and Processing, ICIAP 2022, held in Lecce, Italy, in May 2022. The 96 revised full papers presented in the proceedings set were carefully reviewed and selected from 157 submissions. ICIAP 2022 presents the following Sixteen workshops: Volume I: GoodBrother workshop on visual intelligence for active and assisted livingParts can worth like the Whole - PART 2022Workshop on Fine Art Pattern Extraction and Recognition - FAPERWorkshop on Intelligent Systems in Human and Artificial Perception - ISHAPE 2022Artificial Intelligence and Radiomics in Computer-Aided Diagnosis - AIRCADDeep-Learning and High Performance Computing to Boost Biomedical Applications - DeepHealth Volume II: Human Behaviour Analysis for Smart City Environment Safety - HBAxSCESBinary is the new Black (and White): Recent Advances on Binary Image ProcessingArtificial Intelligence for preterm infants’ healthCare - AI-careTowards a Complete Analysis of People: From Face and Body to Clothes - T-CAPArtificial Intelligence for Digital Humanities - AI4DHMedical Transformers - MEDXFLearning in Precision Livestock Farming - LPLFWorkshop on Small-Drone Surveillance, Detection and Counteraction Techniques - WOSDETCMedical Imaging Analysis For Covid-19 - MIACOVID 2022Novel Benchmarks and Approaches for Real-World Continual Learning - CL4REAL