GLAM-SERP: Building a Graph Learning-Assisted Model for Soft Error Resilience Prediction in GPGPUs
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0859-8_25
Reference22 articles.
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2. Tan, J., Fu, X.: RISE: improving the streaming processors reliability against soft errors in GPGPUs. In: Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques. ACM (2012). https://doi.org/10.1145/2370816.2370846
3. Li, G., et al.: Understanding error propagation in deep learning neural network (DNN) accelerators and applications. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. ACM (2017). https://doi.org/10.1145/3126908.3126964
4. Yang, L., Nie, B., Jog, A., Smirni, E.: Enabling software resilience in GPGPU applications via partial thread protection. In: 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). IEEE (2021). https://doi.org/10.1109/icse43902.2021.00114
5. Kalra, C., Previlon, F., Rubin, N., Kaeli, D.: ArmorAll: compiler-based resilience targeting GPU applications. ACM Trans. Archit. Code Optim. 17(2), 1–24 (2020). https://doi.org/10.1145/3382132
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