About this Research Topic
Extreme Data refers to massive amounts of Big Data that must be queried, communicated, and analyzed in (near) real-time by using a very large number of memory and computing elements. Large repositories and continuous streams of data soon will be processed and analyzed by Exascale computing systems (parallel computers capable of at least one ExaFLOPs) that today are under development. Significant examples are scientific data produced at a rate of hundreds of gigabits-per-second that must be stored, filtered and analyzed; millions of images per day that must be analyzed in parallel; or billions of social data posts queried in real-time on an in-memory components database. Nowadays, traditional disks and commercial storage systems cannot handle the extreme scale of such application’s data and a very large number of cores are needed to process them. Following the need of improvement of current concepts and technologies, this Research Topic aims at focusing on data-intensive algorithms, systems and applications running on systems composed of up to millions of computing elements on which are based the Exascale systems.
Papers included in this Research Topic will discuss
• Parallel hardware and software systems for Big Data storing, processing, and analysis.
• Methods, techniques, and prototypes designed and used to implement Big Data solution on massive HPC and Exascale systems.
• Massively parallel algorithms and applications for machine learning.
• New programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on Exascale systems.
• Innovative applications of Big Data computing.
• Big Data analysis use cases in large scale systems.
Those contributions can pave the way for the exploitation of massive parallelism in processing large repositories of data, promoting high performance and efficiency, and offering powerful operations and mechanisms for processing extreme data sources at high speed and/or in real-time.
Keywords: Big Data, high performance computing, Exascale systems, machine learning algorithms, extreme data processing, parallel programming, scalable computing.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.