Abstract-Memory errors are a major source of reliability problems in current computers. Undetected errors may result in program termination, or, even worse, silent data corruption. Recent studies have shown that the frequency of permanent memory errors is an order of magnitude higher than previously assumed and regularly affects everyday operation.Often, neither additional circuitry to support hardwarebased error detection nor downtime for performing hardware tests can be afforded. In the case of permanent memory errors, a system faces two challenges: detecting errors as early as possible and handling them while avoiding system downtime.To increase system reliability, we have developed RAMpage, an online memory testing infrastructure for commodity x86-64-based Linux servers, which is capable of efficiently detecting memory errors and which provides graceful degradation by withdrawing affected memory pages from further use.We describe the design and implementation of RAMpage and present results of an extensive qualitative as well as quantitative evaluation.

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Dynamic languages such as R are increasingly used to process .large data sets. Here, the R interpreter induces a large memory overhead due to wasteful memory allocation policies. If an application's working set exceeds the available physical memory, the OS starts to swap, resulting in slowdowns of a several orders of magnitude. Thus, memory optimizations for R will be beneficial to many applications. Existing R optimizations are mostly based on dynamic compilation or native libraries. Both methods are futile when the OS starts to page out memory. So far, only a few, data-type or application specific memory optimizations for R exist. To remedy this situation, we present a low-overhead page sharing approach for R that significantly reduces the interpreter's memory overhead. Concentrating on the most rewarding optimizations avoids the high runtime overhead of existing generic approaches for memory deduplication or compression. In addition, by applying knowledge of interpreter data structures and memory allocation patterns, our approach is not constrained to specific R applications and is transparent to the R interpreter. Our page sharing optimization enables us to reduce the memory consumption by up to 53.5% with an average of 18.0% for a set of real-world R benchmarks with a runtime overhead of only 5.3% on average. In cases where page I/O can be avoided, significant speedups are achieved.

Memory errors are a major source of reliability problems in computer systems. Undetected errors may result in program termination or, even worse, silent data corruption. Recent studies have shown that the frequency of permanent memory errors is an order of magnitude higher than previously assumed and regularly affects everyday operation. To reduce the impact of memory errors, we designed RAMpage, a purely software-based infrastructure to assess and circumvent permanent memory errors in a running commodity x86-64 Linux-based system. We briefly describe the design and implementation of RAMpage and present new results from an extensive qualitative and quantitative evaluation. These results show the efficiency of our approach-RAMpage is able to provide a smooth graceful degradation in the presence of permanent memory errors while requiring only a small overhead in terms of CPU time, energy, and memory space.

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