Multi-GPU nodes are increasingly common in the rapidly evolving landscape of exascale supercomputers. On these systems, GPUs on the same node are connected through dedicated networks, with bandwidths up to a few terabits per second. However, gauging performance expectations and maximizing system efficiency is challenging due to different technologies, design options, and software layers. This paper comprehensively characterizes three supercomputers — Alps, Leonardo, and LUMI — each with a unique architecture and design. We focus on performance evaluation of intra-node and inter-node interconnects on up to 4,096 GPUs, using a mix of intra-node and inter-node benchmarks. By analyzing its limitations and opportunities, we aim to offer practical guidance to researchers, system architects, and software developers dealing with multi-GPU supercomputing. Our results show that there is untapped bandwidth, and there are still many opportunities for optimization, ranging from network to software optimization.

Flash-based storage is known to suffer from performance unpredictability due to interference between host-issued I/O and device-side I/O management. SSDs with data placement capabilities, such as Zoned Namespaces (ZNS) and Flexible Data Placement (FDP), expose selective device-side I/O management operations to the host to provide predictable performance. In this paper, we demonstrate that these host-issued I/O management operations lead to performance interference with host-issued I/O. Indeed, we find that the I/O management operations introduced by ZNS and FDP create I/O interference, leading to significant performance losses. Despite the performance implications, we observe that ZNS research frequently uses emulators (over 20 recently published papers), but no emulator currently has function-realistic models for I/O management. To address this gap, we identify ten ZNS I/O management designs, explain how they interfere with I/O, and introduce ConfZNS++, a function-realistic emulator with native I/O management support, providing future research with the capability to explore these designs. Additionally, we introduce two actionable host-managed solutions to reduce ZNS management interference: ZINC, an I/O scheduler prioritizing I/O over I/O management, and the softfinish operation, a host-managed implementation of the finish operation. In our experiments, ZINC reduces reset interference by 56.9%, and softfinish reduces finish interference by 50.7%.

Flash SSDs have become the de-facto choice to deliver high I/O performance to modern data-intensive workloads. These workloads are often deployed in the cloud, where multiple tenants share access to flash-based SSDs. Cloud providers use various techniques, including I/O schedulers available in the Linux kernel, such as \textitBFQ, Multiqueue-Deadline ~(\mqddl ), and \kyber, to ensure certain performance qualities (i.e., service-level agreements, SLAs). Though designed for fast NVMe SSDs, there has not been a systematic study of these schedulers for modern, high-performance SSDs with their unique challenges. In this paper. we systematically characterize the performance, overheads, and scalability properties of Linux storage schedulers on NVMe SSDs with millions of I/O operations/s. We report 23 observations and 5 key findings that indicate that (i) CPU performance is the primary bottleneck with the Linux storage stack with high-performance NVMe SSDs; (ii) Linux I/O schedulers can introduce 63.4% performance overheads with NVMe SSDs; (iii) \kyber and q can deliver 99.3% lower P99 latency than \none or \mqddl schedulers in the presence of multiple interfering workloads. We open-source the scripts and datasets of this work at: https://zenodo.org/records/10599514.