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We present ContextPrefetcher, a host-guided high-performant prefetching framework for near-storage accelerators that prefetches data blocks from storage (e.g., NAND) to device-level RAM. Efficiently prefetching data blocks to device-level RAM reduces storage access costs and improves I/O performance. We introduce a novel abstraction, Cross-layered Context (CLC), a virtual entity that spans across the host and the device and is used for identifying, managing, and tracking active and inactive data such as files, objects (within object stores), or a range of blocks. To support efficient prefetching of actively used CLCs to device memory without incurring near-device resource (memory and compute) bottlenecks, ContextPrefetcher delegates prefetching management to the host, guiding near-device compute to prefetch blocks of active CLC. Finally, ContextPrefetcher facilitates the swift reclamation of blocks associated with inactive CLC. Preliminary evaluation against state-of-the-art near-storage accelerator designs demonstrates performance gains of up to 1.34X. 

We propose OmniCache, a novel caching design for near-storage accelerators that combines near-storage and host memory capabilities to accelerate I/O and data processing. First, OmniCache introduces a “near-cache” approach, maximizing data access to the nearest cache for I/O and processing operations. Second, OmniCache presents collaborative caching for concurrent I/O and data processing by using host and device caches. Third, OmniCache incorporates a dynamic model-driven offloading support, which actively monitors hardware and software metrics for efficient processing across host and device processors. Finally, OmniCache explores the extensive- ability for the newly-introduced CXL, a memory expansion technology. OmniCache demonstrates significant performance gains of up to 3.24X for I/O workloads and 3.06X for data processing workloads. 

We present FusionFS, a direct-access firmware-level in-storage filesystem that exploits the near-storage computational capability for fast I/O and data processing, consequently reducing I/O bottlenecks. In FusionFS, we introduce a new abstraction, CISCOps, that combines multiple I/O and data processing operations into one fused operation and offloaded for near-storage processing. By offloading, CISCOps significantly reduces dominant I/O overheads such as system calls, data movement, communication, and other software overheads. Further, to enhance the use of CISCOps, we introduce MicroTx for fine-grained crash consistency and fast (automatic) recovery of I/O and data processing operations. We also explore scheduling techniques to ensure fair and efficient use of in-storage compute and memory resources across tenants. Evaluation of FusionFS against the state-of-the-art user-level, kernel-level, and firmware-level file systems using microbenchmarks, macrobenchmarks, and real-world applications shows up to 6.12X, 5.09X and 2.07X performance gains, and 2.65X faster recovery for applications.