GPUs are being increasingly adopted as compute accelerators in many domains, spanning
environments from mobile systems to cloud computing. These systems are usually running …

In this work, the influence of the dynamic task scheduling process is examined. Out-of-order
(OoO) implementation processes exhibit remarkable guarantee for task-level parallelism in …

Future multicore processors will be heterogeneous, be increasingly less reliable, and
operate in dynamically changing operating conditions. Such environments will result in a …

Machine learning has been widely applied in various emerging data-intensive applications,
and has to be optimized and accelerated by powerful engines to process very large scale …

General purpose hybrid dataflow/von-Neumann architectures are gaining attraction as
effective parallel platforms. Although different implementations differ in the way they merge …

Many algorithms schedule their work, or tasks, according to a priority order for correctness or
faster convergence. While priority schedulers commonly implement task enqueue and …

The improvements in semiconductor technologies are gradually enabling extreme-scale
systems such as teradevices (ie, chips composed by 1000 billion of transistors), most likely …

Fast simulation of digital circuits is crucial to build modern chips. But RTL (Register-Transfer-
Level) simulators are slow, as they cannot exploit multicores well. Slow simulation lengthens …

As multicore processors become the default, researchers are aggressively looking for
program execution models that make it easier to use the available resources. Multithreaded …

The complexity of High Performance Computing (HPC) systems is increasing in the number
of components and their heterogeneity. Interactions between software and hardware involve …