Even though platforms for general purpose GPU computing like CUDA® (Compute Unified Device Architecture) from NVIDIA® which provides a CUDA API exist, it remains difficult and time consuming to port existing algorithms that were designed for execution on CPUs.
At the current stage, the software is designed for execution by non-expert personnel with common hardware at user's home institution where HPC computing resources are not available.
This control scheme is designed for execution by a multi-agent system at two consecutive stages of static and dynamic scheduling.
Our approach is inspired by a traditional CPU-based code, LAMMPS (Plimpton, 1995), but is implemented within a code that was designed for execution on GPUs from the start (Anderson et al., 2008).
To evaluate whether the goals and ambitions of the Charter have been achieved, three pan-European patient surveys were designed for execution between 2010 and 2012.
This does not put considerable restrictions on the description of the existing protocols as most protocols are designed for execution of experiments in the real physical world by manipulating real physical objects.
Although several other proteogenomic packages (Chapman et al., 2013; Kumar et al., 2013; Risk et al., 2013; Sanders et al., 2011) have been developed in recent years, they were designed for execution on a single workstation.
However, each of these programs is designed for execution on a single computing node, and as such requires a long running time or limits the sensitivity of the alignments they find.
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The algorithm is designed for parallel execution on Graphics Processing Units (GPUs) for near-real-time processing speed.
Users are not troubled by the pausing and resumption of VMs for a short time because the scheduler has been designed for the execution of batch type applications such as scientific applications.
