GangES

Abstract

As technology scales, the hardware reliability challenge affects a broad computing market, rendering traditional redundancy based solutions too expensive. Software anomaly based hardware error detection has emerged as a low cost reliability solution, but suffers from Silent Data Corruptions (SDCs). It is crucial to accurately evaluate SDC rates and identify SDC producing software locations to develop software-centric low-cost hardware resiliency solutions. A recent tool, called Relyzer, systematically analyzes an entire application's resiliency to single bit soft-errors using a small set of carefully selected error injection sites. Relyzer provides a practical resiliency evaluation mechanism but still requires significant evaluation time, most of which is spent on error simulations. This paper presents a new technique called GangES (Gang Error Simulator) that aims to reduce error simulation time. GangES observes that a set or gang of error simulations that result in the same intermediate execution state (after their error injections) will produce the same error outcome; therefore, only one simulation of the gang needs to be completed, resulting in significant overall savings in error simulation time. GangES leverages program structure to carefully select when to compare simulations and what state to compare. For our workloads, GangES saves 57% of the total error simulation time with an overhead of just 1.6% This paper also explores pure program analyses based techniques that could obviate the need for tools such as GangES altogether. The availability of Relyzer+GangES allows us to perform a detailed evaluation of such techniques. We evaluate the accuracy of several previously proposed program metrics. We find that the metrics we considered and their various linear combinations are unable to adequately predict an instruction's vulnerability to SDCs, further motivating the use of Relyzer+GangES style techniques as valuable solutions for the hardware error resiliency evaluation problem

Toward Dynamic Precision Scaling

“…We implement the proof-of-concept offline profiler compromising statistical fault injection and DPS policies using Pin [Luk et al 2005], as an extension to the Pin-based approximate computing framework iACT [Mishra et al 2014]. During offline profiling, we inject two types of faults in the mantissa: We set one mantissa bit (out of 23 for single; 52, for double precision) to 0 (stuck-at-0) or 1 (stuck-at-1) at a time.…”