LogNIC: A High-Level Performance Model for SmartNICs
Zerui Guo, Jiaxin Lin, Yuebin Bai, Daehyeok Kim, Michael Swift, Aditya Akella, and Ming Liu
In Proceedings of 56th IEEE/ACM International Symposium on Microarchitecture, October 2023
SmartNICs have become an indispensable communication fabric and computing substrate in today’s data centers and enterprise clusters, providing in-network computing capabilities for traversed packets and benefiting a range of applications across the system stack. Building an efficient SmartNIC-assisted solution is generally non-trivial and tedious as it requires programmers to understand the SmartNIC architecture, refactor application logic to match the device’s capabilities and limitations, and correlate an application execution with traffic characteristics. A high-level SmartNIC performance model can decouple the underlying SmartNIC hardware device from its offloaded software implementations and execution contexts, thereby drastically simplifying and facilitating the development process. However, prior architectural models can hardly be applied due to their ineptness in dissecting the SmartNIC-offloaded program’s complexity, capturing the nondeterministic overlapping between computation and I/O, and perceiving diverse traffic profiles.
This paper presents the LogNIC model that systematically analyzes the performance characteristics of a SmartNIC-offloaded program. Unlike conventional execution flow-based modeling, LogNIC employs a packet-centric approach that examines SmartNIC execution based on how packets traverse heterogeneous computing domains, on-/off-chip interconnects, and memory subsystems. It abstracts away the low-level device details, represents a deployed program as an execution graph, retains a handful of configurable parameters, and generates latency/throughput estimation for a given traffic profile. It further exposes a couple of extensions to handle multi-tenancy, traffic interleaving, and accelerator peculiarity. We demonstrate the LogNIC model’s capabilities using both commodity SmartNICs and an academic prototype under five application scenarios. Our evaluations show that LogNIC can estimate performance bounds, explore software optimization strategies, and provide guidelines for new hardware designs.
