Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Zhantong Qiu

dblp:417/4889 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2026
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-author · 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
Performance modeling and evaluation · 100%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation
benchmarking
1.012026
Nugget: Portable Program Snippets · HPCA 2026
Performance modeling and evaluation
simulation
1.012026
Nugget: Portable Program Snippets · HPCA 2026
Compilers and program optimization › intermediate representation
LLVM IR
0.312026
Nugget: Portable Program Snippets · HPCA 2026

Methods — techniques the papers use, named apart from their topics

interval analysis · 2.0binary-independent sampling · 2.0
YearPublicationVenuePosition
2026 Nugget: Portable Program Snippets
abstract
Evaluating architectural ideas on realistic workloads is increasingly challenging due to the prohibitive cost of detailed simulation and the lack of portable sampling tools. Existing targeted sampling techniques are often tied to specific binaries, incur significant overhead, and make rapid validation across systems infeasible. To address these limitations, we introduce Nugget, a flexible framework that enables portable sampling across simulators, hardware, architectural differences, and libraries. Nugget leverages LLVM IR to perform binary-independent interval analysis, then generates lightweight, cross-platform executable snippets, nuggets, that can be validated natively on real hardware before use in simulation. This approach decouples samples from specific binaries, dramatically reduces analysis overhead, and allows researchers to iterate on sampling methodologies while efficiently validating samples across diverse systems.
Zhantong Qiu, Mahyar Samani, Jason Lowe-Power
HPCA1