As HPC workloads continue to grow in size and complexity, efficient data management becomes increasingly crucial for the overall performance of the system. This presentation explores the impact of data management on HPC workloads and how critical it is becoming for efficient HPC.
Frequently, test results cannot be duplicated, data sets get misplaced, and the proper handling of data is compromised during storage upgrades or staff changes. The datasets for research projects are continuously expanding, demanding efficient data and storage management. Groundbreaking discoveries often come with their own unique data management challenges. This includes the need to handle large volumes of data, manage data access and sharing, and ensure data consistency and integrity. Increasingly, research is done colaboratively and often across continents. There are often constraints on the data due to: privacy, who funded its collection; as well as access, security and storage costs. The importance of achieving the right data management strategy becomes paramount as the size, and complexiety, of HPC datasets continues to increase.
Join us as we discuss the impact of data management on HPC workflows, survey some of the current research trends and future directions, and explore real-world use cases and best practices from organizations optimizing their data management in support of breakthrough research. The session will explore data management for immediate computational needs as well as alternatives for long-term data access, management, and preservation. This is an interactive session where we invite the audience to share best practices.
Target Audience: Everyone! Whether representing a big organization or themselves, efficient data management is crucial for maximizing system performance ensuring data is preserved and accessible. This presentation delves into the challenges of managing expanding research datasets, emphasizing the need for effective data management strategies by using real-world use cases and best practices.
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