Parallel computing has emerged as an environment for computing inherently parallel and
computation intensive applications. Performance is always a key factor in determining the …

The paper proposes a metric that evaluates the overhead introduced into parallel programs
by the additional operations that parallelism implicitly imposes. We consider the case of …

The actual performance of parallel programs is often disappointing, especially in
comparison to the peak performance offered by the underlying hardware. There are many …

Predicting the execution time of parallel applications in High Performance Computing (HPC)
clusters has served different objectives, including helping developers to find relevant areas …

With the advent of multi-core processors and the growing popularity of local cluster
installations, better understanding of parallel applications behavior becomes a necessity. It …

Although current parallel system performance metrics include the many variants of speedup,
efficiency and isoefficiency, the authors argue that none should be used independently of …

Scientific computing is commonly performed in high performance environments in order to
take advantage of parallelism to solve data-intensive problems. The application …

Performance is often a key factor in determining the success of a parallel software system.
Performance evaluation techniques can be classified into three categories: measurement …

Abstract Model factors provide initial insight into fundamental issues of parallel applications.
These metrics elaborate beyond conventional HPC metrics to indicate whether an …