HARP：实用有效地识别使用片上纠错码的内存芯片中的不可纠正错误
HARP: Practically and Effectively Identifying Uncorrectable Errors in Memory Chips That Use On-Die Error-Correcting Codes
摘要：
用于解决缩放相关的主存储器错误的现有技术从存储器控制器内识别并修复处于错误风险的位。不幸的是，现代主内存芯片在内部使用片上纠错码（片上ECC），片上ECC混淆了存储器控制器对错误的看法，使识别有风险位（即，错误剖析）。为了理解片上ECC导致错误剖析的问题，我们分析性地研究片上ECC如何改变存储器错误出现在内存芯片外部的方式（例如，到存储器控制器）。我们发现，在芯片上的ECC在不同的位位置的错误之间引入了统计依赖性，提出了三个关键的挑战，实际和有效的错误分析。为了解决这三个挑战，我们引入了混合主动-反应分析（HARP），这是一种新的错误分析算法，可以快速实现使用片上ECC的内存芯片中风险位的完全覆盖。HARP将错误分析分为两个阶段：（1）使用现有的分析技术，并对片上ECC机制进行小的修改，以快速识别风险位的子集;以及（2）使用存储器控制器内的辅助ECC，以安全地识别剩余的风险位，如果它们失败。我们的评估表明，HARP更快地实现了所有风险位的完全覆盖（例如，在每个ECC字有2/3/4/5个原始位错误的情况下，平均快20.6%/36.4%/52.9%/62.1%的第99百分位覆盖率），这比两种最先进的基线错误分析算法有时无法实现完全覆盖。我们进行了一个案例研究，每个分析器如何影响系统的整体位出错率（BER）时，使用修复机制，容忍DRAM数据保留错误。我们表明，HARP优于最佳基线算法（例如，对于0.75的原始每比特错误概率乘以3.7x）。
英文摘要：
State-of-the-art techniques for addressing scaling-related main memory errors identify and repair bits that are at risk of error from within the memory controller. Unfortunately, modern main memory chips internally use on-die error correcting codes (on-die ECC) that obfuscate the memory controller's view of errors, complicating the process of identifying at-risk bits (i.e., error profiling). To understand the problems that on-die ECC causes for error profiling, we analytically study how on-die ECC changes the way that memory errors appear outside of the memory chip (e.g., to the memory controller). We show that on-die ECC introduces statistical dependence between errors in different bit positions, raising three key challenges for practical and effective error profiling. To address the three challenges, we introduce Hybrid Active-Reactive Profiling (HARP), a new error profiling algorithm that rapidly achieves full coverage of at-risk bits in memory chips that use on-die ECC. HARP separates error profiling into two phases: (1) using existing profiling techniques with the help of small modifications to the on-die ECC mechanism to quickly identify a subset of at-risk bits; and (2) using a secondary ECC within the memory controller to safely identify the remaining at-risk bits, if and when they fail. Our evaluations show that HARP achieves full coverage of all at-risk bits faster (e.g., 99th-percentile coverage 20.6%/36.4%/52.9%/62.1% faster, on average, given 2/3/4/5 raw bit errors per ECC word) than two state-of-the-art baseline error profiling algorithms, which sometimes fail to achieve full coverage. We perform a case study of how each profiler impacts the system's overall bit error rate (BER) when using a repair mechanism to tolerate DRAM data-retention errors. We show that HARP outperforms the best baseline algorithm (e.g., by 3.7x for a raw per-bit error probability of 0.75).
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