کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11002662 | 1446733 | 2018 | 35 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Exploring the capabilities of support vector machines in detecting silent data corruptions
ترجمه فارسی عنوان
بررسی قابلیت های ماشین های بردار پشتیبانی در تشخیص فساد اطلاعات خاموش
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
چکیده انگلیسی
In this work, we explore a set of novel SDC detectors - by leveraging epsilon-insensitive support vector machine regression - to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based on different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Sustainable Computing: Informatics and Systems - Volume 19, September 2018, Pages 277-290
Journal: Sustainable Computing: Informatics and Systems - Volume 19, September 2018, Pages 277-290
نویسندگان
Omer Subasi, Sheng Di, Leonardo Bautista-Gomez, Prasanna Balaprakash, Osman Unsal, Jesus Labarta, Adrian Cristal, Sriram Krishnamoorthy, Franck Cappello,