3,521 research outputs found
QUASII: QUery-Aware Spatial Incremental Index.
With large-scale simulations of increasingly detailed models and improvement of data acquisition technologies, massive amounts of data are easily and quickly created and collected. Traditional systems require indexes to be built before analytic queries can be executed efficiently. Such an indexing step requires substantial computing resources and introduces a considerable and growing data-to-insight gap where scientists need to wait before they can perform any analysis. Moreover, scientists often only use a small fraction of the data - the parts containing interesting phenomena - and indexing it fully does not always pay off. In this paper we develop a novel incremental index for the exploration of spatial data. Our approach, QUASII, builds a data-oriented index as a side-effect of query execution. QUASII distributes the cost of indexing across all queries, while building the index structure only for the subset of data queried. It reduces data-to-insight time and curbs the cost of incremental indexing by gradually and partially sorting the data, while producing a data-oriented hierarchical structure at the same time. As our experiments show, QUASII reduces the data-to-insight time by up to a factor of 11.4x, while its performance converges to that of the state-of-the-art static indexes
Improving I/O Performance for Exascale Applications through Online Data Layout Reorganization
The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity and record turn-around time. Many of these codes are based on particle-mesh methods and use advanced algorithms, especially dynamic load-balancing and mesh-refinement, to achieve high performance on Exascale machines. Yet, as such algorithms improve parallel application efficiency, they raise new challenges for I/O logic due to their irregular and dynamic data distributions. Thus, while the enormous data rates of Exascale simulations already challenge existing file system write strategies, the need for efficient read and processing of generated data introduces additional constraints on the data layout strategies that can be used when writing data to secondary storage. We review these I/O challenges and introduce two online data layout reorganization approaches for achieving good tradeoffs between read and write performance. We demonstrate the benefits of using these two approaches for the ECP particle-in-cell simulation WarpX, which serves as a motif for a large class of important Exascale applications. We show that by understanding application I/O patterns and carefully designing data layouts we can increase read performance by more than 80 percent
Dynamic Virtual Page-based Flash Translation Layer with Novel Hot Data Identification and Adaptive Parallelism Management
Solid-state disks (SSDs) tend to replace traditional motor-driven hard disks in high-end storage devices in past few decades. However, various inherent features, such as out-of-place update [resorting to garbage collection (GC)] and limited endurance (resorting to wear leveling), need to be reduced to a large extent before that day comes. Both the GC and wear leveling fundamentally depend on hot data identification (HDI). In this paper, we propose a hot data-aware flash translation layer architecture based on a dynamic virtual page (DVPFTL) so as to improve the performance and lifetime of NAND flash devices. First, we develop a generalized dual layer HDI (DL-HDI) framework, which is composed of a cold data pre-classifier and a hot data post-identifier. Those can efficiently follow the frequency and recency of information access. Then, we design an adaptive parallelism manager (APM) to assign the clustered data chunks to distinct resident blocks in the SSD so as to prolong its endurance. Finally, the experimental results from our realized SSD prototype indicate that the DVPFTL scheme has reliably improved the parallelizability and endurance of NAND flash devices with improved GC-costs, compared with related works.Peer reviewe
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Tracking Back References in a Write-Anywhere File System
Many file systems reorganize data on disk, for example to
defragment storage, shrink volumes, or migrate data between
different classes of storage. Advanced file system features such as snapshots, writable clones, and deduplication make these tasks complicated, as moving a single block may require finding and updating dozens, or even hundreds, of pointers to it.
We present Backlog, an efficient implementation of explicit back references, to address this problem. Back references are file system meta-data that map physical block numbers to the data objects that use them. We show that by using LSM-Trees and exploiting the write-anywhere behavior of modern file systems such as NetApp R WAFL R or btrfs, we can maintain back reference meta-data with minimal overhead (one extra disk I/O per 102 block operations) and provide excellent query performance for the common case of queries covering ranges of physically adjacent blocks.Engineering and Applied Science
MODELING AND EVALUATION OF A HYBRID OPTICAL AND MAGNETIC DISK STORAGE ARCHITECTURE
A hybrid storage system combining optical disks and magnetic disks is proposed and evaluated via mathematical models. Unlike most current applications of optical disk technology, which consider static databases or deferred update, this research considers environments with a moderate level of near real-time updates. An example of such an environment is databases for administrative decision support systems (DSS). The proposed hybrid storage system uses a write-once, read-many optical disk device (ODD) for the database and a conventional magnetic disk (MD) for storage of a differential file. Periodically, the differential file is used to refresh the ODD file by writing updated records to free space on the ODD. When available free space on the ODD is exhausted, the file is written to new ODD media - - a reorganization operation. Models of storage cost are developed to determine the optimum refresh cycle time, t*, and optimum reorganization cycle time, T*. Parameters of the model include data file volatility, file size, device costs, and costs for refresh and reorganization. Numerical results indicate that the hybrid system is attractive for a broad range of database environments
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