Graphic Engine Resource Management

Modern consumer-grade 3D graphic cards boast a computation/memory resource that can easily rival or even exceed that of standard desktop PCs. Although these cards are mainly designed for 3D gaming applications, their enormous computational power has attracted developers to port an increasing number of scientific computation programs to these cards, including matrix computation, collision detection, cryptography, database sorting, etc. As more and more applications run on 3D graphic cards, there is a need to allocate the computation/memory resource on these cards among the sharing applications more fairly and efficiently. In this paper, we describe the design, implementation and evaluation of a Graphic Processing Unit (GPU) scheduler based on Deficit Round Robin scheduling that successfully allocates to every process an equal share of the GPU time regardless of their demand. This scheduler, called GERM, estimates the execution time of each GPU command group based on dynamically collected statistics, and controls each process’s GPU command production rate through its CPU scheduling priority. Measurements on the first GERM prototype show that this approach can keep the maximal GPU time consumption difference among concurrent GPU processes consistently below 5 % for a variety of application mixes