Thesis
Enforcing crash consistency of scientific applications in non-volatile main memory systems
Washington State University
Master of Science (MS), Washington State University
Spring 2020
DOI:
https://doi.org/10.7273/000004034
Handle:
https://hdl.handle.net/2376/125152
Abstract
To fully leverage the emerging non-volatile main memory (NVMM) for high-performance computing, programmers need efficient data structures that are aware of NVMM memory models and provide crash consistency. Manual creation of NVMM-aware persistent data structures requires a deep understanding of how to create persistent snapshots of memory objects corresponding to the data structures. Manual creation also requires substantial code modification. These issues make it difficult for even experienced programmers to manually create NVMM-aware data structures. To simplify the process, we design a compiler-assistant technique, NVPath. With the aid of compilers, it automatically generates NVMM-aware persistent data structures that provide the same level of guarantee of crash consistency compared to the baseline code. Compiler assistant code annotation and transformation are general and can be applied to applications using various data structures. Furthermore, it is a gray-box technique which requires minimum users' input. Finally, it keeps the baseline code structure for good readability and maintenance. Our experimental results with real world scientific applications show that the performance of annotated programs is commensurate with the version using the manual code transformation on the Titan supercomputer.
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Details
- Title
- Enforcing crash consistency of scientific applications in non-volatile main memory systems
- Creators
- Tyler Nolen Coy
- Contributors
- Xuechen Zhang (Chair) - Washington State University, School of Engineering and Computer Science (VANC)Xinghui Zhao (Committee Member) - Washington State University, School of Engineering and Computer Science (VANC)Scott Wallace (Committee Member) - Washington State University, School of Engineering and Computer Science (VANC)
- Awarding Institution
- Washington State University
- Academic Unit
- School of Engineering and Computer Science (VANC)
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Identifiers
- 99900890794601842
- Language
- English
- Resource Type
- Thesis