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  1. Host-managed shingled magnetic recording drives (HMSMR) give a capacity advantage to harness the explosive growth of data. Applications where data is sequentially written and randomly read, such as key-value stores based on Log-Structured Merge Trees (LSM-trees), make the HMSMR an ideal solution due to its capacity, predictable performance, and economical cost. However, building an LSMtree based KV store on HM-SMR drives presents severe challenges in maintaining the performance and space efficiency due to the redundant cleaning processes for applications and storage devices (i.e., compaction and garbage collections). To eliminate the overhead of on-disk garbage collections (GC) and improve compaction efficiency,more »this paper presents GearDB, a GC-free KV store tailored for HMSMR drives. GearDB proposes three new techniques: a new on-disk data layout, compaction windows, and a novel gear compaction algorithm. We implement and evaluate GearDB with LevelDB on a real HM-SMR drive. Our extensive experiments have shown that GearDB achieves both good performance and space efficiency, i.e., on average 1:71 faster than LevelDB in random write with a space efficiency of 89.9%.« less
  2. Key-value (KV) stores play an increasingly critical role in supporting diverse large-scale applications in modern data centers hosting terabytes of KV items which even might reside on a single server due to virtualization purpose. The combination of ever growing volume of KV items and storage/application consolidation is driving a trend of high storage density for KV stores. Shingled Magnetic Recording (SMR) represents a promising technology for increasing disk capacity, but it comes at a cost of poor random write performance and severe I/O amplification. Applications/software working with SMR devices need to be designed and optimized in an SMR-friendly manner. Inmore »this work, we present SEALDB, a Log-Structured Merge tree (LSM-tree) based key-value store that is specifically op- timized for and works well with SMR drives via adequately addressing the poor random writes and severe I/O amplification issues. First, for LSM-trees, SEALDB concatenates SSTables of each compaction, and groups them into sets. Taking sets as the basic unit for compactions, SEALDB improves compaction efficiency by mitigating random I/Os. Second, SEALDB creates varying size bands on HM-SMR drives, named dynamic bands. Dynamic bands not only accommodate the storage of sets, but also eliminate the auxiliary write amplification from SMR drives. We demonstrate the advantages of SEALDB via extensive experiments in various workloads. Overall, SEALDB delivers impressive performance improvement. Compared with LevelDB, SEALDB is 3.42× faster on random load due to improved compaction efficiency and eliminated auxiliary write amplification on SMR drives.« less
  3. Free, publicly-accessible full text available May 1, 2023
  4. Abstract We search for gravitational-wave signals associated with gamma-ray bursts (GRBs) detected by the Fermi and Swift satellites during the second half of the third observing run of Advanced LIGO and Advanced Virgo (2019 November 1 15:00 UTC–2020 March 27 17:00 UTC). We conduct two independent searches: a generic gravitational-wave transients search to analyze 86 GRBs and an analysis to target binary mergers with at least one neutron star as short GRB progenitors for 17 events. We find no significant evidence for gravitational-wave signals associated with any of these GRBs. A weighted binomial test of the combined results finds nomore »evidence for subthreshold gravitational-wave signals associated with this GRB ensemble either. We use several source types and signal morphologies during the searches, resulting in lower bounds on the estimated distance to each GRB. Finally, we constrain the population of low-luminosity short GRBs using results from the first to the third observing runs of Advanced LIGO and Advanced Virgo. The resulting population is in accordance with the local binary neutron star merger rate.« less
    Free, publicly-accessible full text available April 1, 2023
  5. Free, publicly-accessible full text available March 1, 2023
  6. Intermediate-mass black holes (IMBHs) span the approximate mass range 100−10 5   M ⊙ , between black holes (BHs) that formed by stellar collapse and the supermassive BHs at the centers of galaxies. Mergers of IMBH binaries are the most energetic gravitational-wave sources accessible by the terrestrial detector network. Searches of the first two observing runs of Advanced LIGO and Advanced Virgo did not yield any significant IMBH binary signals. In the third observing run (O3), the increased network sensitivity enabled the detection of GW190521, a signal consistent with a binary merger of mass ∼150  M ⊙ providing direct evidencemore »of IMBH formation. Here, we report on a dedicated search of O3 data for further IMBH binary mergers, combining both modeled (matched filter) and model-independent search methods. We find some marginal candidates, but none are sufficiently significant to indicate detection of further IMBH mergers. We quantify the sensitivity of the individual search methods and of the combined search using a suite of IMBH binary signals obtained via numerical relativity, including the effects of spins misaligned with the binary orbital axis, and present the resulting upper limits on astrophysical merger rates. Our most stringent limit is for equal mass and aligned spin BH binary of total mass 200  M ⊙ and effective aligned spin 0.8 at 0.056 Gpc −3 yr −1 (90% confidence), a factor of 3.5 more constraining than previous LIGO-Virgo limits. We also update the estimated rate of mergers similar to GW190521 to 0.08 Gpc −3 yr −1 .« less
    Free, publicly-accessible full text available March 1, 2023