This paper highlights the necessary development of new instrumentation tools within the
PaRSE task-based runtime system to leverage the performance of low-rank matrix …

Abstract Graphics Processing Units (GPUs) have been widely used to accelerate artificial
intelligence, physics simulation, medical imaging, and information visualization applications …

Parallel applications performance strongly depends on the number of resources. Although
adding new nodes usually reduces execution time, excessive amounts are often detrimental …

The efficient utilization of heterogeneous computing systems is crucial for scientists and
industrial organizations to execute computationally intensive applications. Task-based …

Programming parallel applications for heterogeneous HPC platforms is much more
straightforward when using the task-based programming paradigm. The simplicity exists …

Heterogeneity is part of HPC infrastructures, not only at the intra-node but at the system
level. Applications with multiple phases with distinct resource necessities can take …

Task-based parallelism has adequately addressed the coding complexity required to fully
exploit the processing power offered by omnipresent hybrid CPU/GPU supercomputers …

Parallel applications executing in contemporary heterogeneous clusters are complex to
code and optimize. The task‐based programming model is an alternative to handle the …

Visualization strategies are a valuable tool in the performance evaluation of HPC
applications. Although the traditional Gantt charts are a widespread and enlightening …

Given the complexity of current supercomputers and applications, being able to trace
application executions to understand their behavior is not a luxury. As constraints, tracing …