论文标题
通过数据内容感知访问来改善相变的内存性能
Improving Phase Change Memory Performance with Data Content Aware Access
论文作者
论文摘要
相变内存(PCM)中写操作的重要特征是其延迟和能量对要编写的数据以及覆盖的内容敏感。我们观察到,与覆盖已知的All-Zeros或All-Onson内容相比,覆盖未知的内存内容可以显着更高的潜伏期和能量。这是因为仅通过在一个方向上对PCM单元进行编程,即使用设置或重置操作,而不是两者都会通过对PCM单元进行编程,从而覆盖了All-Eneros或All-Ons内容。在本文中,我们建议使用数据内容PCM写入(DATACON),这是一种新机制,通过将这些请求重定向到包含包含All-Eseros或All-Onson的内存位置来减少PCM的延迟和能量。 DataCon分为三个步骤。首先,它通过全面考虑要编写的数据中的设定位数以及用于PCM中的设置和重置操作的能量延迟权衡,估计PCM写入访问权限将受益于覆盖已知内容(例如,All-Zeros或All-One)的多少。其次,它将写地址转换为内存中的物理地址,其中包含覆盖最佳的内容类型,并将此翻译记录在表中以供将来访问。我们在工作负载中利用数据访问区域,以最大程度地减少地址翻译开销。第三,它以不干扰常规的读写访问的方式重新定位了使用已知的全Zeros或All-Ons内容的未使用的内存位置。 DataCon仅在绝对必要时才覆盖未知内容。我们使用最先进的机器学习应用程序,规格CPU2017和NAS并行基准的工作负载评估了Datacon。结果表明,与以性能为导向的最佳技术相比,DataCon显着改善了系统性能和内存系统能量消耗。
A prominent characteristic of write operation in Phase-Change Memory (PCM) is that its latency and energy are sensitive to the data to be written as well as the content that is overwritten. We observe that overwriting unknown memory content can incur significantly higher latency and energy compared to overwriting known all-zeros or all-ones content. This is because all-zeros or all-ones content is overwritten by programming the PCM cells only in one direction, i.e., using either SET or RESET operations, not both. In this paper, we propose data content aware PCM writes (DATACON), a new mechanism that reduces the latency and energy of PCM writes by redirecting these requests to overwrite memory locations containing all-zeros or all-ones. DATACON operates in three steps. First, it estimates how much a PCM write access would benefit from overwriting known content (e.g., all-zeros, or all-ones) by comprehensively considering the number of set bits in the data to be written, and the energy-latency trade-offs for SET and RESET operations in PCM. Second, it translates the write address to a physical address within memory that contains the best type of content to overwrite, and records this translation in a table for future accesses. We exploit data access locality in workloads to minimize the address translation overhead. Third, it re-initializes unused memory locations with known all-zeros or all-ones content in a manner that does not interfere with regular read and write accesses. DATACON overwrites unknown content only when it is absolutely necessary to do so. We evaluate DATACON with workloads from state-of-the-art machine learning applications, SPEC CPU2017, and NAS Parallel Benchmarks. Results demonstrate that DATACON significantly improves system performance and memory system energy consumption compared to the best of performance-oriented state-of-the-art techniques.