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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 devicelevel 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 1.34×. 

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 introduce CrossPrefetch, a novel cross-layered I/O prefetching mechanism that operates across the OS and a user-level runtime to achieve optimal performance. Existing OS prefetching mechanisms suffer from rigid interfaces that do not provide information to applications on the prefetch effectiveness, suffer from high concurrency bottlenecks, and are inefficient in utilizing available system memory. CrossPrefetch addresses these limitations by dividing responsibilities between the OS and runtime, minimizing overhead, and achieving low cache misses, lock contentions, and higher I/O performance. CrossPrefetch tackles the limitations of rigid OS prefetching interfaces by maintaining and exporting cache state and prefetch effectiveness to user-level runtimes. It also addresses scalability and concurrency bottlenecks by distinguishing between regular I/O and prefetch operations paths and introduces fine-grained prefetch indexing for shared files. Finally, CrossPrefetch designs low-interference access pattern prediction combined with support for adaptive and aggressive techniques to exploit memory capacity and storage bandwidth. Our evaluation of CrossPrefetch, encompassing microbenchmarks, macrobenchmarks, and real-world workloads, illustrates performance gains of up to 1.22x-3.7x in I/O throughput.We also evaluate CrossPrefetch across different file systems and local and remote storage configurations. 

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 propose OmniCache, a novel caching design for nearstorage 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 modeldriven offloading support, which actively monitors hardware and software metrics for efficient processing across host and device processors. Finally, OmniCache explores the extensibility for 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 uFS, a user-level filesystem semi-microkernel. uFS takes advantage of a high-performance storage development kit to realize a fully-functional, crash-consistent, highly-scalable filesystem,with relative developer ease. uFS delivers scalable high performance with a number of novel techniques: careful partitioning of in-memory and on-disk data structures to enable concurrent access without locking, inode migration for balancing load across filesystem threads, and a dynamic scaling algorithm for determining the number of filesystem threads to serve the current workload. Through measurements, we show that uFS has good base performance and excellent scalability; for example, uFS delivers nearly twice the throughput of ext4 for LevelDB on YCSB workloads.