This paper presents and evaluates a method to predict DRAM uncorrected errors, a leading cause of hardware failures in large-scale HPC clusters. The method uses a random forest classifier, which was trained and evaluated using error logs from two years of production of the MareNostrum 3 supercomputer. By enabling the system to take measures to mitigate node failures, our method reduces lost compute time by up to 57%, a net saving of 21,000 node-hours per year. We release all source code as open source.We also discuss and clarify aspects of methodology that are essential for a DRAM prediction method to be useful in practice. We explain why standard evaluation metrics, such as precision and recall, are insufficient, and base the evaluation on a cost-benefit analysis. This methodology can help ensure that any DRAM error predictor is clear from training bias and has a clear cost-benefit calculation.

This paper summarizes our two-year study of corrected and uncorrected errors on the MareNostrum 3 supercomputer, covering 2000 billion MB-hours of DRAM in the field. The study analyzes 4.5 million corrected and 71 uncorrected DRAM errors and it compares the reliability of DIMMs from all three major memory manufacturers, built in three different technologies. Our work has two sets of contributions. First, we illustrate the complexity of in-field DRAM error analysis and demonstrate the limitations of various widely-used methods and metrics. For example, we show that average error rates, errors per MB-hour and mean time between failures can provide volatile and unreliable results even after long periods of error logging, leading to incorrect conclusions about DRAM reliability. Second, we present formal statistical methods that overcome many of the limitations of the current approaches. The methods that we present are simple to understand and implement, reliable and widely accepted in the statistical community. Overall, our study alerts the community about the need to, firstly, question the current practice in quantifying DRAM reliability and, secondly, to select a proper analysis approach for future studies. Our strong recommendations are to focus on metrics with a practical value that could be easily related to system reliability, and to select methods that provide stable results, ideally supported with statistical significance. CCS CONCEPTS • Computer systems organization → Reliability; • Mathematics of computing → Probability and statistics.