Popular software key-value stores such as LevelDB and RocksDB are often tailored for efficient writing. Yet, they tend to also perform well on read operations. This is because while data is initially stored in a format that favors writes, it is later transformed by the DB in the background into a format that better accommodates reads. Write-optimized key-value stores can still block writes. This happens when those background workers cannot keep up with the foreground insertion workload.This paper advocates for a hardware-accelerated key-value store, enabling performance-critical operations, like background data reorganization and queries, to execute directly on storage instead of a host as existing key-value stores do. This better hides background work latency, prevents it from blocking foreground writes, and improves overall I/O efficiency. Our prototype, called KV-CSD, is a key-value based computational storage device consisting of an NVMe SSD and a System-on-a-Chip (SoC) that implements an ordered key-value store atop the SSD. Through offloaded processing, KV-CSD streamlines data insertion, reduces host-device data movement for both background data reorganization and query processing, and shows up to 10.6× lower write times and up to 7.4× faster queries compared to the current state-of-the-art software key-value stores on a real scientific dataset.

Key–value (KV) software has proven useful to a wide variety of applications including analytics, time-series databases, and distributed file systems. To satisfy the requirements of diverse workloads, KV stores have been carefully tailored to best match the performance characteristics of underlying solid-state block devices. Emerging KV storage device is a promising technology for both simplifying the KV software stack and improving the performance of persistent storage-based applications. However, while providing fast, predictable put and get operations, existing KV storage devices do not natively support range queries that are critical to all three types of applications described above. In this article, we present KVRangeDB, a software layer that enables processing range queries for existing hash-based KV solid-state disks (KVSSDs). As an effort to adapt to the performance characteristics of emerging KVSSDs, KVRangeDB implements log-structured merge tree key index that reduces compaction I/O, merges keys when possible, and provides separate caches for indexes and values. We evaluated the KVRangeDB under a set of representative workloads, and compared its performance with two existing database solutions: a Rocksdb variant ported to work with the KVSSD, and Wisckey, a key–value database that is carefully tuned for conventional block devices. On filesystem aging workloads, KVRangeDB outperforms Wisckey by 23.7× in terms of throughput and reduce CPU usage and external write amplifications by 14.3× and 9.8×, respectively.

Modern High-Performance Computing (HPC) data centers routinely store massive data sets resulting in millions of directories and billions of files. To efficiently search and sift through these files and directories we present the Grand Unified File Index (GUFI), a novel file system metadata index that enables both privileged and regular users to rapidly locate and characterize data sets of interest. GUFI uses a hierarchical index that preserves file access permissions such that the index can be securely accessed by users while still enabling efficient, advanced analysis of storage system usage by cluster administrators. Compared with the current state-of-the-art indexing for file system metadata, GUFI is able to provide speedups of 1.5× to 230× for queries executed by administrators on a real production file system namespace. Queries executed by users, which typically cannot rely on cluster-wide indexing, see even greater speedups using GUFI.

The introduction of NVDIMM memory devices has encouraged the use of DRAM/NVM based hybrid memory systems to increase the memory-per-core ratio in compute nodes and obtain possible energy and cost benefits. However, Non-Volatile Memory (NVM) is slower than DRAM in terms of read/write latency. This difference in performance will adversely affect memory-bound applications. Traditionally, data prefetching at the hardware level has been used to increase the number of cache hits to mitigate performance degradation. However, software (SW) prefetching has not been used effectively to reduce the effects of high memory access latencies. Also, the current cache hierarchy and hardware (HW) prefetching are not optimized for a hybrid memory system. We hypothesize that HW and SW prefetching can complement each other in placing data in caches and the Data Translation Look-aside Buffer (DTLB) prior to their references, and by doing so adaptively, highly varying access latencies in a DRAM/NVM hybrid memory system are taken into account. This work contributes an adaptive SW prefetch method based on the characterization of read/write/unroll prefetch distances for NVM and DRAM. Prefetch performance is characterized via custom benchmarks based on STREAM2 specifications in a multicore MPI runtime environment and compared to the performance of the standard SW prefetch pass in GCC. Furthermore, the effects of HW prefetching on kernels executing on hybrid memory system are evaluated. Experimental results indicate that SW prefetching targeted to populate the DTLB results in up to 26% performance improvement when symbiotically used in conjunction with HW prefetching, as opposed to only HW prefetching. Based on our findings, changes to GCC's prefetch-loop-arrays compiler pass are proposed to take advantage of DTLB prefetching in a hybrid memory system for kernels that are frequently used in HPC applications.

InfiniBand is the de facto networking technology for commodity HPC clusters and has been widely deployed. However, most production large-scale InfiniBand clusters use simple routing schemes such as the destination-mod-k routing to route traffic, which may result in degraded communication performance. In this work, we investigate using the OpenFlow-style Software-Defined Networking (SDN) technology to overcome the routing deficiency in InfiniBand. We design an enhanced InfiniBand with OpenFlow-style SDN capability and demonstrate a use case that illustrates how the SDN capability can be exploited in HPC clusters to improve the system and application performance. Finally, we quantify the potential benefits of InfiniBand with OpenFlow-style SDN capability in balancing the network load by simulating job traces from production HPC clusters. The results indicate that InfiniBand with SDN capability can achieve much better network load balancing than traditional InfiniBand for HPC clusters.