This paper proposes a novel optimization framework for the Data-Flow Graph Language
(DFGL), a dependence-based notation for macro-dataflow model which can be used as an …

This paper describes an approach to performance optimization using modified macro
dataflow graphs, which contain nodes representing the loops and data involved in the …

Many important applications including machine learning, molecular dynamics, and
computational fluid dynamics, use sparse data. Processing sparse data leads to non-affine …

This work offers in-depth analysis of three different darknet datasets captured in 2004, 2006
and 2008 to provide insights into the nature of backscatter traffic. Moreover, we analyzed …

Applications running on clusters of shared-memory computers are often implemented using
OpenMP+ MPI. Productivity can be vastly improved using task-based programming, a …

On shared-memory multicore machines, classic two-way recursive divide-and-conquer
algorithms are implemented using common fork-join based parallel programming paradigms …

Writing scientific applications for modern multicore machines is a challenging task. There
are a myriad of hardware solutions available for many different target applications, each …

Sparse computations are important in scientific computing. Many scientific applications
compute on sparse data. Data is said to be sparse if it has a relatively small number of non …

Application tuning is the one of the major hurdles on the road to exascale computing. Tuning
is often directed at a specific architecture or towards some specific tuning goal. As currently …

Scientific applications, especially legacy applications, contain a wealth of scientific
knowledge. As hardware changes, applications need to be ported to new architectures and …