Cache-aware reliability evaluation through LLVM-based analysis and fault injection

Abstract

1-Reliability evaluation is a high costly process that is mainly carried out through fault injection or by means of analytical techniques. While the analytical techniques are fast but inaccurate, the fault injection is more accurate but extremely time consuming. This paper presents an hybrid approach combining analytical and fault injection techniques in order to evaluate the reliability of a computing system, by considering errors that affect both the data and the instruction cache. Compared to existing techniques, instead of targeting the hardware model of the cache (e.g., VHDL description), we only consider the running application (i.e., the software layer). The proposed approach is based on the Low-Level Virtual Machine (LLVM) framework coupled with a cache emulator. As input, the tool requires the application source code, the cache size and policy, and the target microprocessor instruction set. The main advantage of the proposed approach is the achieved speed up quantified in magnitude orders compared to existing fault injection techniques. For the validation, we compare the simulation results to those obtained with an FPGA-based fault injector. The similarity of the results proves the accuracy of the approach.

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“…It is important to get an accurate picture of these proportions; for example, an application that experiences a large percentage of SDCs may require algorithmic error detection mechanisms, at the expense of runtime overhead. A concern in the HPC community is that a significant number of resilience studies have been based on this FI method [3,4,6,17,18,25,31,32,[34][35][36] (including our own work), which can potentially skew FI results and, in some cases, lead to incorrect conclusions. There has been research done in showing these inaccuracies.…”

Reliability Evaluation of CNC Machine Tools Considering Competing Failures of Fault Failure Data and Machining Accuracy Degradation Data

“…Zhang proposed an improved Bootstrap sampling method for the problem of difficulty in obtaining failure data and applied this method to reliability evaluation [3]. Kool proposed a technique to combine simulation data with fault monitoring data for reliability evaluation, which alleviates the high cost of reliability evaluation [4]. Wang proposed a risk assessment method for CNC lathes based on faulty data and the combination of fuzzy set theory and gray theory, which avoids the shortcomings of traditional RPN analysis and improves the reliability of CNC lathes [5].…”