Architectural support for persistent memory systems

The long stated vision of persistent memory is set to be realized with the release of 3D XPoint memory by Intel and Micron. Persistent memory, as the name suggests, amalgamates the persistence (non-volatility) property of storage devices (like disks) with byte-addressability and low latency of memory. These properties of persistent memory coupled with its accessibility through the processor load/store interface enable programmers to design in-memory persistent data structures. An important challenge in designing persistent memory systems is to provide support for maintaining crash consistency of these in-memory data structures. Crash consistency is necessary to ensure the correct recovery of program state after a crash. Ordering is a primitive that can be used to design crash consistent programs. It provides guarantees on the order of updates to persistent memory. Atomicity can also be used to design crash consistent programs via two primitives. First, as an atomic durability primitive which guarantees that in the presence of system crashes updates are made durable atomically, which means either all or none of the updates are made durable. Second, in the form of ACID transactions that guarantee atomic visibility and atomic durability. Existing systems do not support ordering, let alone atomic durability or ACID. In fact, these systems implement various performance enhancing optimizations that deliberately reorder updates to memory. Moreover, software in these systems cannot explicitly control the movement of data from volatile cache to persistent memory. Therefore, any ordering requirement has to be enforced synchronously which degrades performance because program execution is stalled waiting for updates to reach persistent memory. This thesis aims to provide the design principles and efficient implementations for three crash consistency primitives: ordering, atomic durability and ACID transactions. A set of persistency models have been proposed recently which provide support for the ordering primitive. This thesis extends the taxonomy of these models by adding buffering, which allows the hardware to enforce ordering in the background, as a new layer of classification. It then goes on show how the existing implementation of a buffered model degenerates to a performance inefficient non-buffered model because of the presence of conflicts and proposes efficient solutions to eliminate or limit the impact of these conflicts with minimal hardware modifications. This thesis also proposes the first implementation of a buffered model for a server class processor with multi-banked caches and multiple memory controllers. Write ahead logging (WAL) is a commonly used approach to provide atomic durability. This thesis argues that existing implementations ofWAL in software are not only inefficient, because of the fine grained ordering dependencies, but also waste precious execution cycles to implement a fundamentally data movement task. It then proposes ATOM, a hardware log manager based on undo logging that performs the logging operation out of the critical path. This thesis presents the design principles behind ATOM and two techniques that optimize its performance. These techniques enable the memory controller to enforce fine grained ordering required for logging and to even perform logging in some cases. In doing so, ATOM significantly reduces processor stall cycles and improves performance. The most commonly used abstraction employed to atomically update persistent data is that of durable transactions with ACID (Atomicity, Consistency, Isolation and Durability) semantics that make updates within a transaction both visible and durable atomically. As a final contribution, this thesis tackles the problem of providing efficient support for durable transactions in hardware by integrating hardware support for atomic durability with hardware transactional memory (HTM). It proposes DHTM (durable hardware transactional memory) in which durability is considered as a first class design constraint. DHTM guarantees atomic durability via hardware redo-logging, and integrates this logging support with a commercial HTM to provide atomic visibility. Furthermore, DHTM leverages the same logging infrastructure to extend the supported transaction size, from being L1-limited to the LLC, with minor changes to the coherence protocol

Architectural Principles for Database Systems on Storage-Class Memory

Database systems have long been optimized to hide the higher latency of storage media, yielding complex persistence mechanisms. With the advent of large DRAM capacities, it became possible to keep a full copy of the data in DRAM. Systems that leverage this possibility, such as main-memory databases, keep two copies of the data in two different formats: one in main memory and the other one in storage. The two copies are kept synchronized using snapshotting and logging. This main-memory-centric architecture yields nearly two orders of magnitude faster analytical processing than traditional, disk-centric ones. The rise of Big Data emphasized the importance of such systems with an ever-increasing need for more main memory. However, DRAM is hitting its scalability limits: It is intrinsically hard to further increase its density. Storage-Class Memory (SCM) is a group of novel memory technologies that promise to alleviate DRAM’s scalability limits. They combine the non-volatility, density, and economic characteristics of storage media with the byte-addressability and a latency close to that of DRAM. Therefore, SCM can serve as persistent main memory, thereby bridging the gap between main memory and storage. In this dissertation, we explore the impact of SCM as persistent main memory on database systems. Assuming a hybrid SCM-DRAM hardware architecture, we propose a novel software architecture for database systems that places primary data in SCM and directly operates on it, eliminating the need for explicit IO. This architecture yields many benefits: First, it obviates the need to reload data from storage to main memory during recovery, as data is discovered and accessed directly in SCM. Second, it allows replacing the traditional logging infrastructure by fine-grained, cheap micro-logging at data-structure level. Third, secondary data can be stored in DRAM and reconstructed during recovery. Fourth, system runtime information can be stored in SCM to improve recovery time. Finally, the system may retain and continue in-flight transactions in case of system failures. However, SCM is no panacea as it raises unprecedented programming challenges. Given its byte-addressability and low latency, processors can access, read, modify, and persist data in SCM using load/store instructions at a CPU cache line granularity. The path from CPU registers to SCM is long and mostly volatile, including store buffers and CPU caches, leaving the programmer with little control over when data is persisted. Therefore, there is a need to enforce the order and durability of SCM writes using persistence primitives, such as cache line flushing instructions. This in turn creates new failure scenarios, such as missing or misplaced persistence primitives. We devise several building blocks to overcome these challenges. First, we identify the programming challenges of SCM and present a sound programming model that solves them. Then, we tackle memory management, as the first required building block to build a database system, by designing a highly scalable SCM allocator, named PAllocator, that fulfills the versatile needs of database systems. Thereafter, we propose the FPTree, a highly scalable hybrid SCM-DRAM persistent B+-Tree that bridges the gap between the performance of transient and persistent B+-Trees. Using these building blocks, we realize our envisioned database architecture in SOFORT, a hybrid SCM-DRAM columnar transactional engine. We propose an SCM-optimized MVCC scheme that eliminates write-ahead logging from the critical path of transactions. Since SCM -resident data is near-instantly available upon recovery, the new recovery bottleneck is rebuilding DRAM-based data. To alleviate this bottleneck, we propose a novel recovery technique that achieves nearly instant responsiveness of the database by accepting queries right after recovering SCM -based data, while rebuilding DRAM -based data in the background. Additionally, SCM brings new failure scenarios that existing testing tools cannot detect. Hence, we propose an online testing framework that is able to automatically simulate power failures and detect missing or misplaced persistence primitives. Finally, our proposed building blocks can serve to build more complex systems, paving the way for future database systems on SCM