Since the mid-1990s, researchers have been trying to use machine-learning-based
approaches to solve a number of different compiler optimization problems. These …

Automatic parallelization of sequential programs combined with auto-tuning is an alternative
to manual parallelization. With wider research directions and the increased number of …

Auto-tuning is a popular approach to program optimization: it automatically finds good
configurations of a program's so-called tuning parameters whose values are crucial for …

We have designed and implemented a Grid RPC system called OmniRPC, for parallel
programming in cluster and grid environments. While OmniRPC inherits its API from Ninf, the …

There is a consensus that exascale systems should operate within a power envelope of
20MW. Consequently, energy conservation is still considered as the most crucial constraint if …

Heterogeneous architectures characterize today hardware ranging from super-computers to
smartphones. However, in spite of this importance, programming such systems is still …

Stencil computations comprise an important class of kernels in many scientific computing
applications. As the diversity of both architectures and programming models grow …

The objective of data compression is to avoid redundancy in order to reduce the size of the
data to be stored or transmitted. In some scenarios, data compression may help to increase …

To improve performance of large-scale scientific applications, scientists or tuning experts
make various empirical attempts to change compiler options, program parameters or even …

The process of performance tuning is time consuming and costly even if it is carried out
automatically. It is crucial to learn from the experience of experts. Our long-term goal is to …