Scheduling periodic real-time communication in multi-GPU systems

Multi-GPU systems have become a popular architecture for high-throughput processing of streaming data. In many such systems, data transfers inside the compute nodes are becoming a performance bottleneck due to insufficient bandwidth. The problem is even more acute for real-time systems, which sacrifice utilization and efficiency in order to achieve predictable and analyzable execution. Data transfer over the interconnect of a compute node is most efficient when it is streamed on multiple paths in parallel. However, this mode of operation greatly complicates the transfer time analysis due to the effects of bus contention, especially if the data transfers are asynchronous. This work presents a new scheduler for periodic data transfers with deadlines that uses the system interconnect efficiently. The scheduler analyzes the data transfer requirements and their time constraints and produces a verifiable schedule that transfers the data in parallel. Experiments on realistic systems show that our method achieves up to 74% higher system throughput than the classic scheduling methods.