EDBT 2026 Demo / reviewers in the wild / expert
Zhantong Qiu
dblp:417/4889
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation
benchmarking |
1.0 | 1 | 2026 | Nugget: Portable Program Snippets · HPCA 2026 |
Performance modeling and evaluation
simulation |
1.0 | 1 | 2026 | Nugget: Portable Program Snippets · HPCA 2026 |
Compilers and program optimization › intermediate representation
LLVM IR |
0.3 | 1 | 2026 | Nugget: Portable Program Snippets · HPCA 2026 |
Methods — techniques the papers use, named apart from their topics
interval analysis · 2.0binary-independent sampling · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Nugget: Portable Program SnippetsabstractEvaluating 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 |
HPCA | 1 |