VLDB 2026 Research / reviewers in the wild / expert
Songyuan Sui
dblp:356/7974
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0004-1989-8700ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Hardware accelerators and domain-specific architectures · 77% High-performance computing · 12% Performance modeling and evaluation · 12% | |
| Computer networks
1 paper |
Software-defined and programmable networks · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software-defined and programmable networks
programmable data plane |
0.7 | 1 | 2023 | Unleashing SmartNIC Packet Processing Performance in P4 · SIGCOMM 2023 |
Hardware accelerators and domain-specific architectures
network accelerator |
0.7 | 1 | 2023 | Unleashing SmartNIC Packet Processing Performance in P4 · SIGCOMM 2023 |
Hardware accelerators and domain-specific architectures › network accelerator
SmartNIC |
0.7 | 1 | 2023 | Unleashing SmartNIC Packet Processing Performance in P4 · SIGCOMM 2023 |
High-performance computing
performance optimization |
0.2 | 1 | 2023 | Unleashing SmartNIC Packet Processing Performance in P4 · SIGCOMM 2023 |
Performance modeling and evaluation › performance tuning
profile-guided optimization |
0.2 | 1 | 2023 | Unleashing SmartNIC Packet Processing Performance in P4 · SIGCOMM 2023 |
Methods — techniques the papers use, named apart from their topics
profile-guided optimization · 1.3automated performance tuning · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Unleashing SmartNIC Packet Processing Performance in P4abstractSmartNICs are on the rise as a packet processing platform, with the trend towards a uniform P4 programming model. However, unleashing SmartNIC packet processing performance in P4 is a formidable task. Traditional SmartNIC optimizations rely on low-level program tuning, but P4 abstractions operate at one level above. At the same time, today's P4 optimizations primarily focus on resource packing rather than performance tuning. We develop Pipeleon, an automated performance optimization framework for P4 programmable SmartNICs. We introduce techniques that are tailored to the performance characteristics of SmartNICs, and further leverage dynamic workload patterns for profile-guided optimization. Pipeleon pinpoints program hotspots at the P4 level and computes runtime optimization plans to specialize the program layout based on the latest profile. We have prototyped Pipeleon and applied it to optimize two popular P4 SmartNICs---Nvidia BlueField2 and Netronome Agilio CX---as well as a software SmartNIC emulator extended based on BMv2. Our results show that Pipeleon significantly improves SmartNIC packet processing performance in realistic scenarios. Jiarong Xing, Yiming Qiu 0001, Kuo-Feng Hsu, Songyuan Sui, Khalid Manaa, Omer Shabtai, Yonatan Piasetzky, Matty Kadosh, Arvind Krishnamurthy, T. S. Eugene Ng, Ang Chen 0001 |
SIGCOMM | 4 |