Data that often contain unchanging records is becoming increasingly important. Many data
sources, such as historical archives, sensor readings, health systems, and machine logs, do …

With the advancement of exascale computing, the amount of scientific data is increasing day
by day. Efficient data access is necessary for scientific discoveries. Unfortunately, the I/O …

MapReduce brought on the Big Data revolution. However, its impact on scientific data
analyses has been limited because of fundamental limitations in its data and programming …

FasTensor is a generic parallel programming model for big data analyses with user-defined
operations. FasTensor exploits the structural locality in the multidimensional arrays to …

Storage systems have not kept the same technology improvement rate as computing
systems. As applications produce more and more data, I/O becomes the limiting factor for …

With the rapid advances in supercomputing and numerical simulations, the output data of
scientific computing is expanding rapidly, bringing tough challenges for data sharing and …

Les simulations paralllèles sont devenues des outils indispensables dans de nombreux
domaines scientifiques. Pour simuler des phénomènes complexes, ces simulations sont …

Exascale HPC systems often utilize accelerators like GPUs to accelerate entire or parts of
applications. Often, large-scale applications struggle to use the available resources fully …

Complex indexing techniques are needed to reduce the time of analyzing massive scientific
datasets, but generating these indexing data structures can be very time consuming. In this …

Abstract Vector Particle-In-Cell (VPIC) is one of the fastest plasma simulation codes in the
world, with particle numbers ranging from one trillion on the first petascale system …