high performance computing on graphics processing units: hgpu.org

The simulation is a primary step on the evaluation process of modern networked systems. The scalability and efficiency of such a tool in view of increasing complexity of the emerging networks is a key to derive valuable results. The discrete event simulation is recognized as the most scalable model that copes with both parallel and distributed architecture. Nevertheless, the recent hardware provides new heterogeneous computing resources that can be exploited in parallel.The main scope of this thesis is to provide a new mechanisms and optimizations that enable efficient and scalable parallel simulation using heterogeneous computing node architecture including multicore CPU and GPU. To address the efficiency, we propose to describe the events that only differs in their data as a single entry to reduce the event management cost. At the run time, the proposed hybrid scheduler will dispatch and inject the events on the most appropriate computing target based on the event descriptor and the current load obtained through a feedback mechanisms such that the hardware usage rate is maximized. Results have shown a significant gain of 100 times compared to traditional CPU based approaches. In order to increase the scalability of the system, we propose a new simulation model, denoted as general purpose coordinator-master-worker, to address jointly the challenge of distributed and parallel simulation at different levels. The performance of a distributed simulation that relies on the GP-CMW architecture tends toward the maximal theoretical efficiency in a homogeneous deployment. The scalability of such a simulation model is validated on the largest European GPU-based supercomputer.