Modern data-intensive applications often generate large amounts of low precision float data
with a limited range of values. Despite the prevalence of such data, there is a lack of an …

Reproducibility of research results is a fundamental quality criterion in science; thus,
computer architecture effects on simulation results must be determined. Here, we investigate …

In the past decade, many approaches have been suggested to execute ML workloads on a
DBMS. However, most of them have looked at in-DBMS ML from a training perspective …

Parallel aggregation is a ubiquitous operation in data analytics that is expressed as GROUP
BY in SQL, reduce in Hadoop, or segment in TensorFlow. Parallel aggregation starts with an …

Nowadays simulations can produce petabytes of data to be stored in parallel filesystems or
large-scale databases. This data is accessed over the course of decades often by thousands …

High-performance computing applications are increasingly integrating checkpointing
libraries for reproducibility analytics. However, capturing an entire checkpoint history for …

Abstract The Internet of Things (IoT) enables connections of trillions of sensors and data
collection for connectivity and analytics. The amount of IoT-generated data has exploded …

Variations in computational infrastructures, including operating systems, software versions,
and hardware architectures, introduce variability in neuroimaging analyses that could affect …

Parallel aggregation is a ubiquitous operation in data analytics that is expressed as GROUP
BY in SQL, reduce in Hadoop, or segment in TensorFlow. Parallel aggregation starts with an …

During the last few decades, we transitioned into the data-driven era, where scientific
models are being computed on supercomputers and large datacenters. The …