Graphics Processing Units (GPUs) have become widely used to accelerate scientific applications;
therefore, it is important that Computer Science and Computer Engineering curricula include the
fundamentals of parallel computing with GPUs. Regarding the practical part of the training, one
important concern is how to introduce GPUs into a laboratory: installing GPUs in all the computers of
the lab may not be affordable, while sharing a remote GPU server among several students may result
in a poor learning experience because of its associated overhead.
In this paper we propose a solution to address this problem: the use of the rCUDA (remote CUDA)
middleware, which enables programs being executed in a computer to make concurrent use of GPUs
located in remote servers. Hence, students would be able to concurrently and transparently share a
single remote GPU from their local machines in the laboratory without having to log into the remote
server. In order to demonstrate that our proposal is feasible, we present results of a real scenario. The
results show that the cost of the laboratory is noticeably reduced while the learning experience quality
is maintained.Reaño González, C.; Silla Jiménez, F. (2015). Reducing the Costs of Teaching CUDA in Laboratories while Maintaining the Learning Experience Quality. En INTED2015 Proceedings. IATED. 3651-3660. http://hdl.handle.net/10251/70229S3651366
