VLDB 2026 Research / reviewers in the wild / expert
Christopher Rocca
dblp:392/2953
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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 |
GPUs and heterogeneous computing · 50% Interconnection networks and networks-on-chip · 50% | |
| Network and information security
1 paper |
Hardware security and side channels · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
GPUs and heterogeneous computing
GPU architecture |
0.8 | 1 | 2024 | Uncovering Real GPU NoC Characteristics: Implications on Interconnect Architecture · MICRO 2024 |
Interconnection networks and networks-on-chip
on-chip interconnect |
0.8 | 1 | 2024 | Uncovering Real GPU NoC Characteristics: Implications on Interconnect Architecture · MICRO 2024 |
Hardware security and side channels
side-channel attack |
0.2 | 1 | 2024 | Uncovering Real GPU NoC Characteristics: Implications on Interconnect Architecture · MICRO 2024 |
Hardware security and side channels › side-channel attack
timing side channel |
0.2 | 1 | 2024 | Uncovering Real GPU NoC Characteristics: Implications on Interconnect Architecture · MICRO 2024 |
Methods — techniques the papers use, named apart from their topics
latency and bandwidth analysis · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Uncovering Real GPU NoC Characteristics: Implications on Interconnect ArchitectureabstractA critical component of high-throughput processors such as GPUs is the network-on-chip (NoC) that interconnects the large number of cores and the memory partitions together. In this work, we provide a detailed analysis, in terms of latency and bandwidth, of real GPU NoC across several generations of modern NVIDIA GPUs. Our analysis identifies how non-uniform latency exists between the cores and the memory partitions based on their physical location in the GPU. The non-uniformity can result in up to approximately 70 % difference in on-chip latency. In comparison, the bandwidth provided from the cores to the memory partitions is approximately uniform. However, recent GPUs that consist of multiple GPU “partitions” present different on-chip latency and bandwidth characteristics when communicating between the partitions. Based on our analysis of real GPU interconnect, we discuss potential implications including its impact on timing used in side-channel attacks as well as NoC microarchitectures. We show how the non-uniform latency can be exploited in a timing side-channel attack within a GPU as the core location impacts performance (or timing). In addition, proper understanding (and proper assumptions) of GPU NoC is critical to ensure a network that does not bottleneck the overall system performance. Zhixian Jin, Christopher Rocca, Hans Kasan, Minsoo Rhu, Ali Bakhoda, Tor M. Aamodt, John Kim 0001 |
MICRO | 2 |