| Article Details | ||
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| Title: | PRISM: predicting resilience of GPU applications using statistical methods | |
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| Alternative Article URLs: | No | |
| Authors: | Cham Kalra |
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| Fritz Previlon |
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| Xiangyu Li |
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| Norman Rubin |
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| David R. Kaeli |
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| Sharing: | Research produced artifacts | |
| Verification: | Authors have verified information | |
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| DBLP Key: | conf/sc/KalraPLRK18 | |
| Author Comments: | PRISM provides a systematic approach to predict failures in GPU programs. PRISM extracts micro-architecture agnostic features to characterize program resiliency, and serves as an effective predictor to drive our statistical model. PRISM can predict failures in applications without running exhaustive fault injection campaigns, thereby reducing the error estimation effort. | |