Improving the Performance and Endurance of Persistent Memory with Loose-Ordering Consistency

Persistent memory provides high-performance data persistence at main memory. Memory writes need to be performed in strict order to satisfy storage consistency requirements and enable correct recovery from system crashes. Unfortunately, adhering to such a strict order significantly degrades system performance and persistent memory endurance. This paper introduces a new mechanism, Loose-Ordering Consistency (LOC), that satisfies the ordering requirements at significantly lower performance and endurance loss. LOC consists of two key techniques. First, Eager Commit eliminates the need to perform a persistent commit record write within a transaction. We do so by ensuring that we can determine the status of all committed transactions during recovery by storing necessary metadata information statically with blocks of data written to memory. Second, Speculative Persistence relaxes the write ordering between transactions by allowing writes to be speculatively written to persistent memory. A speculative write is made visible to software only after its associated transaction commits. To enable this, our mechanism supports the tracking of committed transaction ID and multi-versioning in the CPU cache. Our evaluations show that LOC reduces the average performance overhead of memory persistence from 66.9% to 34.9% and the memory write traffic overhead from 17.1% to 3.4% on a variety of workloads.Comment: This paper has been accepted by IEEE Transactions on Parallel and Distributed System

Fine-Grain Checkpointing with In-Cache-Line Logging

Non-Volatile Memory offers the possibility of implementing high-performance, durable data structures. However, achieving performance comparable to well-designed data structures in non-persistent (transient) memory is difficult, primarily because of the cost of ensuring the order in which memory writes reach NVM. Often, this requires flushing data to NVM and waiting a full memory round-trip time. In this paper, we introduce two new techniques: Fine-Grained Checkpointing, which ensures a consistent, quickly recoverable data structure in NVM after a system failure, and In-Cache-Line Logging, an undo-logging technique that enables recovery of earlier state without requiring cache-line flushes in the normal case. We implemented these techniques in the Masstree data structure, making it persistent and demonstrating the ease of applying them to a highly optimized system and their low (5.9-15.4\%) runtime overhead cost.Comment: In 2019 Architectural Support for Programming Languages and Operating Systems (ASPLOS 19), April 13, 2019, Providence, RI, US

Persistent Buffer Management with Optimistic Consistency

Finding the best way to leverage non-volatile memory (NVM) on modern database systems is still an open problem. The answer is far from trivial since the clear boundary between memory and storage present in most systems seems to be incompatible with the intrinsic memory-storage duality of NVM. Rather than treating NVM either solely as memory or solely as storage, in this work we propose how NVM can be simultaneously used as both in the context of modern database systems. We design a persistent buffer pool on NVM, enabling pages to be directly read/written by the CPU (like memory) while recovering corrupted pages after a failure (like storage). The main benefits of our approach are an easy integration in the existing database architectures, reduced costs (by replacing DRAM with NVM), and faster peak-performance recovery

Rethinking the I/O Stack for Persistent Memory

Modern operating systems have been designed around the hypotheses that (a) memory is both byte-addressable and volatile and (b) storage is block addressable and persistent. The arrival of new Persistent Memory (PM) technologies, has made these assumptions obsolete. Despite much of the recent work in this space, the need for consistently sharing PM data across multiple applications remains an urgent, unsolved problem. Furthermore, the availability of simple yet powerful operating system support remains elusive. In this dissertation, we propose and build The Region System – a high-performance operating system stack for PM that implements usable consistency and persistence for application data. The region system provides support for consistently mapping and sharing data resident in PM across user application address spaces. The region system creates a novel IPI based PMSYNC operation, which ensures atomic persistence of mapped pages across multiple address spaces. This allows applications to consume PM using the well understood and much desired memory like model with an easy-to-use interface. Next, we propose a metadata structure without any redundant metadata to reduce CPU cache flushes. The high-performance design minimizes the expensive PM ordering and durability operations by embracing a minimalistic approach to metadata construction and management. To strengthen the case for the region system, in this dissertation, we analyze different types of applications to identify their dependence on memory mapped data usage, and propose user level libraries LIBPM-R and LIBPMEMOBJ-R to support shared persistent containers. The user level libraries along with the region system demonstrate a comprehensive end-to-end software stack for consuming the PM devices