Over the last few years, GPUs have become common in computing. However, current GPUs are not designed for a shared environment like a cloud, creating a number of challenges whenever a GPU must be multiplexed between multiple users. In particular, the round-robin scheduling used by today's GPUs does not distribute the available GPU computation time fairly among applications. Most of the previous work addressing this problem resorted to scheduling all GPU computation in software, which induces high overhead. While there is a GPU scheduler called NEON which reduces the scheduling overhead compared to previous work, NEON's accounting mechanism frequently disables GPU access for all but one application, resulting in considerable overhead if that application does not saturate the GPU by itself.
In this paper, we present LoGA, a novel accounting mechanism for GPU computation time. LoGA monitors the GPU's state to detect GPU-internal context switches, and infers the amount of GPU computation time consumed by each process from the time between these context switches. This method allows LoGA to measure GPU computation time consumed by appl ... mehrications while keeping all applications running concurrently. As a result, LoGA achieves a lower accounting overhead than previous work, especially for applications that do not saturate the GPU by themselves. We have developed a prototype which combines LoGA with the pre-existing NEON scheduler. Experiments with that prototype have shown that LoGA induces no accounting overhead while still delivering accurate measurements of applications' consumed GPU computation time.