BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121114T203000Z
DTEND:20121114T210000Z
LOCATION:255-EF
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Extreme-scale scientific applications are at a significant risk of being hit by soft errors on future supercomputers. To better understand soft error vulnerabilities in scientific applications, we have built an empirical fault injection and consequence analysis tool - BIFIT - to evaluate how soft errors impact applications. BIFIT is designed with capability to inject faults at specific targets: execution point and data structure. We apply BIFIT to three scientific applications and investigate their vulnerability to soft errors. We classify each application's individual data structures in terms of their vulnerabilities, and generalize these classifications.  Our study reveals that these scientific applications have a wide range of sensitivities to both the time and the location of a soft error. Yet, we are able to identify relationships between vulnerabilities and classes of data structures. These classifications can be used to apply appropriate resiliency solutions to each data structure within an application.
SUMMARY:Classifying Soft Error Vulnerabilities in Extreme-Scale Scientific Applications Using a Binary Instrumentation Tool
PRIORITY:3
END:VEVENT
END:VCALENDAR
BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121114T203000Z
DTEND:20121114T210000Z
LOCATION:255-EF
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Extreme-scale scientific applications are at a significant risk of being hit by soft errors on future supercomputers. To better understand soft error vulnerabilities in scientific applications, we have built an empirical fault injection and consequence analysis tool - BIFIT - to evaluate how soft errors impact applications. BIFIT is designed with capability to inject faults at specific targets: execution point and data structure. We apply BIFIT to three scientific applications and investigate their vulnerability to soft errors. We classify each application's individual data structures in terms of their vulnerabilities, and generalize these classifications.  Our study reveals that these scientific applications have a wide range of sensitivities to both the time and the location of a soft error. Yet, we are able to identify relationships between vulnerabilities and classes of data structures. These classifications can be used to apply appropriate resiliency solutions to each data structure within an application.
SUMMARY:Classifying Soft Error Vulnerabilities in Extreme-Scale Scientific Applications Using a Binary Instrumentation Tool
PRIORITY:3
END:VEVENT
END:VCALENDAR