In many domains, the previous decade was characterized by increasing data volumes and
growing complexity of data analyses, creating new demands for batch processing on …

Many data-intensive scientific analysis techniques require global domain traversal, which
over the years has been a bottleneck for efficient parallelization across distributed-memory …

Supervised learning of Convolutional Neural Networks (CNNs), also known as supervised
Deep Learning, is a computationally demanding process. To find the most suitable …

While the HPC community is working towards the development of the first Exaflop computer
(expected around 2020), after reaching the Petaflop milestone in 2008 still only few HPC …

The adequate management of scientific workflow applications strongly depends on the
availability of accurate performance models of sub-tasks. Numerous approaches use …

Heterogeneous computing systems provide high performance and energy efficiency.
However, to optimally utilize such systems, solutions that distribute the work across host …

Autoscaling strategies achieve efficient and cheap executions of scientific workflows running
in the cloud by determining appropriate type and amount of virtual machine instances to use …

We describe an approach that uses combinatorial optimization and machine learning to
share the work between the host and device of heterogeneous computing systems such that …

Rapid analysis of DNA sequences is important in preventing the evolution of different
viruses and bacteria during an early phase, early diagnosis of genetic predispositions to …

This paper deals with the problem of autoscaling for cloud computing scientific workflows.
Autoscaling is a process in which the infrastructure scaling (ie determining the number and …