CUDALAUNCH NVPROF CODE

PIConGPU, Particle In Cell on GPUs, is an open source simulations framework for plasma and laser-plasma physics used to develop advanced particle accelerators for radiation therapy of cancer, high energy physics and photon science. This allows the user to see the progress of the behavior of the applications during their lifetime. Furthermore, our tool provides these statistics more » during the entire runtime of the tool as a time series and not just aggregate statistics at the end of the application run. Most of the performance counter results are the same in both vendor tools and our tool when using LDMS to collect these results. Also, we discuss our current validation results. In this report, we discuss the current limitations in the NVIDIA monitoring tools, how we overcame such limitations, and present an overview of the tool we built to monitor GPU performance in LDMS and its capabilities. Therefore, we chose to develop a GPU monitoring capability within the same framework. Sandia has developed CPU application monitoring capability within LDMS. The Light-Weight Distributed Metric System (LDMS) at Sandia is an infrastructure widely adopted for large-scale systems and application monitoring. Since NVIDIA GPUs are currently the most commonly implemented in HPC applications and systems, NVIDIA tools are the solution for performance monitoring.

CUDALAUNCH NVPROF CODE

To deliver the best performance on a GPU, we need to create monitoring tools to ensure that we optimize the code to get the most performance and ef?ciency out of a GPU. They are viewed by many as a technology facilitator for the surge in ?elds like machine learning and Convolutional Neural Networks.

GPUs are now a fundamental accelerator for many high-performance computing applications.

Cudalaunch nvprof