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Computational storage adds computing to storage devices, providing potential benefits in offload, data-reduction, and lower energy. Successful computational SSD architectures should match growing flash bandwidth, which in turn requires high SSD DRAM memory bandwidth. This creates a memory wall scaling problem, resulting from SSDs’ stringent power and cost constraints. A survey of recent computational SSD research shows that many computational storage offloads are suited to stream computing. To exploit this opportunity, we propose a novel general-purpose computational SSD and core architecture, called ASSASIN (Architecture Support for Stream computing to Accelerate computatIoNal Storage). ASSASIN provides a unified set of compute engines between SSD DRAM and the flash array. This eliminates the SSD DRAM bottleneck by enabling direct computing on flash data streams. ASSASIN further employs a crossbar to achieve performance even when flash data layout is uneven and preserve independence for page layout decisions in the flash translation layer. With stream buffers and scratchpad memories, ASSASIN core’s memory hierarchy and instruction set extensions provide superior low-latency access at low-power and effectively keep streaming flash data out of the in-SSD cache-DRAM memory hierarchy, thereby solving the memory wall. Evaluation shows that ASSASIN delivers 1.5x - 2.4x speedup for offloaded functions compared to state-of-the-art computational SSD architectures. Further, ASSASIN’s streaming approach yields 2.0x power efficiency and 3.2x area efficiency improvement. And these performance benefits at the level of computational SSDs translate to 1.1x - 1.5x end-to-end speedups on data analytics workloads.
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FlatFlash: Exploiting the Byte-Accessibility of SSDs within a Unified Memory-Storage Hierarchy
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 which we rethink the design primitives of crash consistency of several representative software systems that require data persistence, such as file systems and databases. Our evaluation with a variety of applications demonstrates that, compared to the current unified memory-storage systems, FlatFlash improves the performance for memory-intensive applications by up to 2.3x, reduces the tail latency for latency-critical applications by up to 2.8x, scales the throughput for transactional database by up to 3.0x, and decreases the meta-data persistence overhead for file systems by up to 18.9x. FlatFlash also improves the cost-effectiveness by up to 3.8x compared to DRAM-only systems, while enhancing the SSD lifetime significantly.
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- Award ID(s):
- 1850317
- PAR ID:
- 10132033
- Date Published:
- Journal Name:
- Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems
- Page Range / eLocation ID:
- 971 to 985
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3297858.3304061
