Abstract
Virtualization techniques have shown to report benefits to data centers and other computing facilities. In this regard, virtual machines not only allow reducing the size of the computing infrastructure while increasing overall resource utilization but virtualizing individual components of computers may also provide significant benefits. This is the case, for example, for the remote GPU virtualization technique, implemented in several frameworks during the last years.
In this paper we present an initial implementation of a new middleware for the remote virtualization of another component of computers: the CPU itself. Our proposal uses remote accelerators to perform computations that were initially intended to be carried out in the local CPUs, doing so transparently to the application and without having to modify its source code. By making use of the OpenBLAS and FFTW libraries as case studies to show the performance gains of our proposal, we carry out a performance evaluation targeting several system configurations comprising Xeon processors as well as Ethernet and InfiniBand QDR, FDR, and EDR network adapters in addition to NVIDIA Tesla K40 GPUs. Results not only demonstrate that the new middleware is feasible, but they also show that mathematical libraries may experience a significant speed up, despite of having to move data forth and back to/from remote servers.
