Data management techniques

Abstract

Today, it is projected that data storage and management is becoming one of the key challenges in order to achieve ultrascale computing for several reasons. First, data is expected to grow exponentially in the coming years and this progression will imply that disruptive technologies will be needed to store large amounts of data and more importantly to access it in a timely manner. Second, the improvement of computing elements and their scalability are shifting application execution from CPU bound to I/O bound. This creates additional challenges for significantly improving the access to data to keep with computation time and thus avoid high-performance computing (HPC) from being underutilized due to large periods of I/O activity. Third, the two initially separate worlds of HPC that mainly consisted on one hand of simulations that are CPU bound and on the other hand of analytics that mainly perform huge data scans to discover information and are I/O bound are blurring. Now, simulations and analytics need to work cooperatively and share the same I/O infrastructure.