Christopher Rocca

dblp:392/2953 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
GPUs and heterogeneous computing
GPU architecture
0.812024
Uncovering Real GPU NoC Characteristics: Implications on Interconnect Architecture · MICRO 2024
Interconnection networks and networks-on-chip
on-chip interconnect
0.812024
Uncovering Real GPU NoC Characteristics: Implications on Interconnect Architecture · MICRO 2024
Hardware security and side channels
side-channel attack
0.212024
Uncovering Real GPU NoC Characteristics: Implications on Interconnect Architecture · MICRO 2024
Hardware security and side channels › side-channel attack
timing side channel
0.212024
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
YearPublicationVenuePosition
2024 Uncovering Real GPU NoC Characteristics: Implications on Interconnect Architecture
abstract
A 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
MICRO2