Efficient Reliability Support for Hardware Multicast-Based Broadcast in GPU-enabled Streaming Applications

摘要

Streaming applications, which are data-intensive, have been extensively run on High-Performance Computing (HPC) systems to seek the higher performance and scalability. These applications typically utilize broadcast operations to disseminate in real-time data from a single source to multiple workers, each being a multi-GPU based computing site. State-of-the-art broadcast operations take advantage of InfiniBand (IB) hardware multicast (MCAST) and NVIDIA GPUDirect features to boost inter-node communications performance and scalability. The IB MCAST feature works only with the IB Unreliable Datagram (UD) mechanism and consequently provides unreliable communication for applications. Higher-level libraries and/or runtime environments must handle and provide reliability explicitly. However, handling reliability at that level can be a performance bottleneck for streaming applications. In this paper, we analyze the specific requirements of streaming applications and the performance bottlenecks involved in handling reliability. We show that the traditional Negative-Acknowledgement (NACK) based approach requires the broadcast sender to perform retransmissions for lost packets, degrading streaming throughput. To alleviate this issue, we propose a novel Remote Memory Access (RMA) based scheme to provide high-performance reliability support at the MPI-level. In the proposed scheme, the receivers themselves (as opposed to the sender) retrieve lost packets through RMA operations. Furthermore, we provide an analytical model to illustrate the memory requirements of the proposed RMA-based scheme. Our experimental results show that the proposed scheme introduces nearly no overhead compared to the existing solutions. In a micro-benchmark with injected failures (to simulate unreliable network environments), the proposed scheme shows up to 45% reduction in latency compared to the existing NACK-based scheme. Moreover, with a synthetic streaming benchmark, our design also shows up to a 56% higher broadcast rate compared to the traditional NACK-based scheme on a GPU-dense Cray CS-Storm system with up to 88 NVIDIA K80 GPU cards.