Affiliation:
1. NVIDIA
2. University of Illinois at Urbana-Champaign
3. Intel Labs
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
Funder
Division of Computer and Network Systems
Publisher
Association for Computing Machinery (ACM)
Cited by
8 articles.
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1. Related Works;Synthesis Lectures on Engineering, Science, and Technology;2023
2. SOFIA: An automated framework for early soft error assessment, identification, and mitigation;Journal of Systems Architecture;2022-10
3. Revisiting Symptom-Based Fault Tolerant Techniques against Soft Errors;Electronics;2021-12-04
4. PARIS: Predicting application resilience using machine learning;Journal of Parallel and Distributed Computing;2021-06
5. Understanding a program's resiliency through error propagation;Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming;2021-02-17