Zhaobin Zhu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Zhaobin Zhu has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Sarah Neuwirth
Johannes Gutenberg University Mainz, Mainz, Germany
4 shared publications - Chen Wang
Nanyang technological university, Singapore, Singapore
2 shared publications - Andreas Henkel
Johannes Gutenberg University Mainz, Mainz, Germany
1 shared publication - Anthony Kougkas
Illinois Institute of Technology, Chicago, IL, USA
1 shared publication - James G. Bandy
Pacific Northwest National Laboratory (PNNL), Richmond, USA
1 shared publication - Kathryn Mohror
Lawrence Livermore National Laboratory, Livermore, CA, USA
1 shared publication
Latest Publications
- Open Access
- 25 January 2026
- Published by Association for Computing Machinery (ACM)
- p. 399-406
Exploring I/O Performance and Power Consumption Trade-offs in Production Environments
- 25 January 2026
- Published by Association for Computing Machinery (ACM)
- p. 399-406
Open AccessExploring I/O Performance and Power Consumption Trade-offs in Production Environments
- Open Access
- 15 November 2025
- Published by Association for Computing Machinery (ACM)
- p. 1415-1423
Quantifying AWS S3 I/O Performance Boundaries Using the Roofline Model
- 15 November 2025
- Published by Association for Computing Machinery (ACM)
- p. 1415-1423
Open AccessQuantifying AWS S3 I/O Performance Boundaries Using the Roofline Model
- Open Access
- 22 June 2025
- Published by Association for Computing Machinery (ACM)
- p. 1-6
Advancing HPC Performance Modeling with an Interactive, Automated and Tool-Agnostic ML-Driven Workflow
- 22 June 2025
- Published by Association for Computing Machinery (ACM)
- p. 1-6
Open AccessAdvancing HPC Performance Modeling with an Interactive, Automated and Tool-Agnostic ML-Driven Workflow
- 3 June 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-12
VerifyIO: Verifying Adherence to Parallel I/O Consistency Semantics
High-performance computing (HPC) applications generate and consume substantial amounts of data, typically managed by parallel file systems. These applications access file systems either through the POSIX interface or by using highlevel I/O libraries. While the POSIX consistency model remains dominant in HPC, emerging file systems and popular I/O libraries increasingly adopt alternative consistency models that relax semantics in various ways, creating significant challenges for correctness and portability. This paper addresses these challenges by proposing a trace-driven I/O consistency verification workflow, implemented in our open-source tool, VerifyIO, which collects execution traces, detects data conflicts, and verifies proper synchronization against specified consistency models. Our extensive evaluation of 91 test case executions across three widely used I/O libraries with four I/O consistency models reveals critical consistency issues at both application and implementation levels.
- 3 June 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-12
VerifyIO: Verifying Adherence to Parallel I/O Consistency Semantics
High-performance computing (HPC) applications generate and consume substantial amounts of data, typically managed by parallel file systems. These applications access file systems either through the POSIX interface or by using highlevel I/O libraries. While the POSIX consistency model remains dominant in HPC, emerging file systems and popular I/O libraries increasingly adopt alternative consistency models that relax semantics in various ways, creating significant challenges for correctness and portability. This paper addresses these challenges by proposing a trace-driven I/O consistency verification workflow, implemented in our open-source tool, VerifyIO, which collects execution traces, detects data conflicts, and verifies proper synchronization against specified consistency models. Our extensive evaluation of 91 test case executions across three widely used I/O libraries with four I/O consistency models reveals critical consistency issues at both application and implementation levels.