Using flash-based solid state drives (SSDs) as main memory has been proposed as a practical solution towards scaling memory capacity for data-intensive applications. However, almost all existing approaches rely on the paging mechanism to move data between SSDs and host DRAM. This inevitably incurs significant performance overhead and extra I/O traffic. Thanks to the byte-addressability supported by the PCIe interconnect and the internal memory in SSD controllers, it is feasible to access SSDs in both byte and block granularity today. Exploiting the benefits of SSD's byte-accessibility in today's memory-storage hierarchy is, however, challenging as it lacks systems support and abstractions for programs. In this paper, we present FlatFlash, an optimized unified memory-storage hierarchy, to efficiently use byte-addressable SSD as part of the main memory. We extend the virtual memory management to provide a unified memory interface so that programs can access data across SSD and DRAM in byte granularity seamlessly. We propose a lightweight, adaptive page promotion mechanism between SSD and DRAM to gain benefits from both the byte-addressable large SSD and fast DRAM concurrently and transparently, while avoiding unnecessary page movements. Furthermore, we propose an abstraction of byte-granular data persistence to exploit the persistence nature of SSDs, upon whichmore »
Unified Holistic Memory Management Supporting Multiple Big Data Processing Frameworks over Hybrid Memories
To process real-world datasets, modern data-parallel systems often require extremely large amounts of memory, which are both costly and energy inefficient. Emerging non-volatile memory (NVM) technologies offer high capacity compared to DRAM and low energy compared to SSDs. Hence, NVMs have the potential to fundamentally change the dichotomy between DRAM and durable storage in Big Data processing. However, most Big Data applications are written in managed languages and executed on top of a managed runtime that already performs various dimensions of memory management. Supporting hybrid physical memories adds a new dimension, creating unique challenges in data replacement. This article proposes Panthera, a semantics-aware, fully automated memory management technique for Big Data processing over hybrid memories. Panthera analyzes user programs on a Big Data system to infer their coarse-grained access patterns, which are then passed to the Panthera runtime for efficient data placement and migration. For Big Data applications, the coarse-grained data division information is accurate enough to guide the GC for data layout, which hardly incurs overhead in data monitoring and moving. We implemented Panthera in OpenJDK and Apache Spark. Based on Big Data applications’ memory access pattern, we also implemented a new profiling-guided optimization strategy, which is transparent to more »
- Publication Date:
- NSF-PAR ID:
- 10338161
- Journal Name:
- ACM Transactions on Computer Systems
- Volume:
- 39
- Issue:
- 1-4
- Page Range or eLocation-ID:
- 1 to 38
- ISSN:
- 0734-2071
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3511211
