发表机构
University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SKYE通过专用线程间接访问和细粒度控制PM访问，实现高写带宽利用率和可扩展性，在YCSB上性能提升2.5-5倍，写吞吐量扩展3.9倍。
AI 中文摘要
为持久内存（PM）构建的最先进的键值存储提供了低延迟，因为它们允许应用程序线程直接访问PM上的数据，并依赖硬件来管理多个非易失性DIMM（NVDIMM）。虽然这提供了低延迟，但导致低吞吐量和可扩展性。性能下降是因为PM硬件需要对PM访问进行细粒度控制；例如，如果太多线程并发写入PM，吞吐量会下降。我们提出了SKYE，一种写优化的PM键值存储，实现了高吞吐量和可扩展性。SKYE基于对所有PM访问保持细粒度控制的核心思想，并实现了高PM写带宽利用率。为实现这一点，SKYE偏离了当前实践，向应用程序提供间接访问；应用程序向SKYE发送请求，SKYE使用专用线程代表它们访问PM。SKYE不依赖硬件管理的PM，而是控制数据在单个NVDIMM上的放置方式。SKYE利用多种介质来避免PM过载，并限制远程NUMA访问以实现可扩展的吞吐量。我们表明，在单个NVDIMM上，SKYE在标准Yahoo云服务基准测试（YCSB）上比最先进的PM存储性能提高2.5-5倍。在四个NUMA节点上的四个NVDIMM中，SKYE获得了约86%的PM写带宽，其写吞吐量扩展了3.9倍。
英文摘要
State-of-the-art key-value stores built for persistent memory (PM) provide low latency as they allow application threads to directly access the data on PM and rely on hardware to manage multiple non-volatile DIMMs (NVDIMMs). While this provides low latency, it results in low throughput and scalability. Performance degrades because PM hardware requires fine-grained control over PM accesses; for example, throughput degrades if too many threads write to PM concurrently. We present SKYE, a write-optimized PM key-value store that achieves high throughput and scalability. SKYE builds on the central idea of maintaining fine-grained control over all PM accesses and obtains high PM write-bandwidth utilization. To achieve this, SKYE deviates from current practice and provides indirect access to applications; applications send requests to SKYE, which uses dedicated threads to access PM on their behalf. Instead of relying on hardware-managed PM, SKYE controls how data is placed on individual NVDIMMs. SKYE leverages multiple media to avoid overloading PM and limits remote NUMA accesses for scalable throughput. We show that on a single NVDIMM, SKYE outperforms state-of-the-art PM stores by 2.5-5x on the standard Yahoo Cloud Serving Benchmark (YCSB). With four NVDIMMs across four NUMA nodes, SKYE obtains about 86% of PM write bandwidth, and its write throughput scales by 3.9x.
