The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
NVBitFI: Dynamic Fault Injection for GPUs
Abstract
GPUs have found wide acceptance in domains such as high-performance computing and autonomous vehicles, which require fast processing of large amounts of data along with provisions for reliability, availability, and safety. A key component of these dependability characteristics is the propagation of errors and their eventual effect on system outputs. In addition to analytical and simulation models, fault injection is an important technique that can evaluate the effect of errors on a complete computing system running the full software stack. However, the complexity of modern GPU systems and workloads challenges existing fault injection tools. Some tools require the recompilation of source code that may not be available, struggle to handle dynamic libraries, lack support for modern GPUs, or add unacceptable performance overheads. We introduce the NVBitFI tool for fault injection into GPU programs. In contrast with existing tools, NVBitFI performs instrumentation of code dynamically and selectively to instrument the minimal set of target dynamic kernels; as it requires no access to source code, NVBitFI provides improvements in performance and usability. The NVBitFI tool is publicly available for download and use at https://github.com/NVlabs/nvbitfi.
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“The solution we designed is flexible, allowing the framework to integrate a different FI tool (for instance the recent [22], [23] and new ones that will emerge), based on the adopted development environment, to produce the error models required to support the FES appropriately.”
From Fast and Accurate Error Simulation for CNNs Against Soft Errors (2023) · cites this paper as [23]
Citations
Cites
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Analyzing CUDA workloads using a detailed GPU simulator
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SASSIFI: An architecture-level fault injection tool for GPU application resilience evaluation
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Self-citationVilla, O., Stephenson, M., Nellans, D. W., & Keckler, S. W. (2019) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3352460.3358307 · Cited by 169
Self-citationGPU-Qin: A methodology for evaluating the error resilience of GPGPU applications
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ML-Based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection
Jha, S., Banerjee, S. S., Tsai, T. K., Hari, S. K. S., Sullivan, M. B., Kalbarczyk, Z., Keckler, S. W., & Iyer, R. K. (2019) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2019.00025 · Cited by 128
Self-citationHauberk: Lightweight Silent Data Corruption Error Detector for GPGPU
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Understanding Error Propagation in GPGPU Applications
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Flexible software profiling of GPU architectures
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Self-citationAnalyzing soft-error vulnerability on GPGPU microarchitecture
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SPEC ACCEL: A Standard Application Suite for Measuring Hardware Accelerator Performance
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Understanding error propagation in GPGPU applications
Li, G., Pattabiraman, K., Cher, C.-Y., & Bose, P. K. (2016) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.5555/3014904.3014932 · Cited by 51
Fault Site Pruning for Practical Reliability Analysis of GPGPU Applications
Nie, B., Yang, L., Jog, A., et al. (2018) · Proceedings of the ... annual International Symposium on Microarchitecture/Proceedings of the annual International Symposium on Microarchitecture · doi:10.1109/micro.2018.00066 · Cited by 50
Calculating Architectural Vulnerability Factors for Spatial Multi-Bit Transient Faults
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SIFI: AMD southern islands GPU microarchitectural level fault injector
Vallero, A., Gizopoulos, D., & Carlo, S. D. (2017) · IEEE International Symposium on On-Line Testing and Robust System Design (IOLTS) · doi:10.1109/iolts.2017.8046209 · Cited by 25
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Research works printed but not linked: 7
- 2021, PTX Parallel Thread Execution ISA Version 1 4
- 2021, CUDA-GDB
- 2021, CUDA Parallel Computing Platform
- 2021, CUDA Binary Utilities
- 2021, GREEN500 LIST - NOVEMBER 2020
- 2021, TOP500 LIST - NOVEMBER 2020
- 2021, Whitepaper Introducing AMD CDNA Architecture - The All-New AMD GPU Architecture for the Modern Era of HPC & AI
Cited by
Cross-Layer Reliability Evaluation and Efficient Hardening of Large Vision Transformers Models
Roquet, L., Fernandes dos Santos, F., Rech, P., Traiola, M., Sentieys, O., & Kritikakou, A. (2024) · Proceedings of the 61st ACM/IEEE Design Automation Conference · doi:10.1145/3649329.3655688 · Cited by 16
Fast and Accurate Error Simulation for CNNs Against Soft Errors
Bolchini, C., Cassano, L., Miele, A., & Toschi, A. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2022.3184274 · Cited by 1
“The solution we designed is flexible, allowing the framework to integrate a different FI tool (for instance the recent [22], [23] and new ones that will emerge), based on the adopted development environment, to produce the error models required to support the FES appropriately.”
Method
Metadata
- Authors
- Tsai, T., Hari, S. K. S., Sullivan, M., Villa, O., & Keckler, S. W.
- Affiliations
- Nvidia (United Kingdom) (GB)
- Venue
- 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 284-291
- ISSN
- 1530-0889 (ISSN-L)
- OpenAlex
- W3187552919
- Cited by
- 93
- References
- 21
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 21 · 2023: 19 · 2022: 20 · 2021: 1
- FWCI
- 14.10
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Dependable Systems & Networks/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Radiation Effects in Electronics
- Keywords
- fault injection tool, dynamic instrumentation, GPU kernels, dependability, error propagation, performance overhead
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
