Pdsw Conference

The PDSW Conference, held at the McCormick Place Convention Center in Chicago, is set to address the critical challenges in large-scale data storage and processing. This workshop is organized in conjunction with the SC26 conference and is a collaborative effort with prestigious institutions, including the IEEE Computer Society and the Association for Computing Machinery. With a focus on efficient data management, PDSW builds upon a rich legacy of previous workshops aimed at enhancing scientific productivity in various computing environments, including traditional HPC and emerging frameworks like Cloud, AI/ML, and Big Data analysis. The workshop aims to foster research and development by inviting contributions of original manuscripts that introduce novel algorithms, architectures, significant case studies, and reproducibility assessments of prior works. Emphasis is placed on community collaboration for problem identification and the development of shared tools to promote transparency and collaborative progress.The conference will explore a range of topics, including scalable architectures for distributed data storage, innovative data processing models for parallel computing, and performance analysis techniques. It will also cover cloud-based models, data integrity methodologies, programming frameworks for big data, and hybrid cloud data processing. Participants will delve into advancements in storage technologies, data reduction techniques, and metadata management, as well as the integration of computation within memory and storage systems. Additionally, discussions will focus on alternative storage models, productivity tools for data-intensive computing, and effective data movement strategies across compute-intensive components. The call for papers encourages detailed submissions that include artifact descriptions, which will undergo a stringent double-anonymous peer review process. Selected papers will be recognized and published in the SC25 Workshop Proceedings, showcasing the collaborative efforts of the research community in tackling the evolving challenges in data management and processing.