Expressions for Batched Searching of Sequential and Hierarchical Files

Batching yields significant savings in access costs in sequential, tree-structured, and random files. A direct and simple expression is developed for computing the average number of records/pages accessed to satisfy a batched query of a sequential file. The advantages of batching for sequential and random files are discussed. A direct equation is provided for the number of nodes accessed in unbatched queries of hierarchical files. An exact recursive expression is developed for node accesses in batched queries of hierarchical files. In addition to the recursive relationship, good, closed-form upper- and lower-bound approximations are provided for the case of batched queries of hierarchical files

Batched Searching in Database Organizations

Savings in the number of page accesses due to batching on sequential, tree-structured, and random files are well known and have been reported in the literature. This paper asserts that substantial savings can also be obtained in database organizations by batching the requests for records (in queries), and also by batching intermediate processing requests while traversing the database. A simple database having two interrelated files is used to demonstrate such savings. For the simple database, three variations on batching are reported and compared with the case of unbatched requests. New mathematical expressions have been developed for the batched cases as well as for the unbatched case, and the savings are demonstrated with some example problems. As an extension, larger databases will enjoy even greater savings due to batching. The paper also discusses several strategies for applying the batching approach to current databases, and the advantages of emerging very large main memories for the batching approach

Galley: A New Parallel File System for Parallel Applications

Most current multiprocessor file systems are designed to use multiple disks in parallel, using the high aggregate bandwidth to meet the growing I/O requirements of parallel scientific applications. Most multiprocessor file systems provide applications with a conventional Unix-like interface, allowing the application to access those multiple disks transparently. This interface conceals the parallelism within the file system, increasing the ease of programmability, but making it difficult or impossible for sophisticated application and library programmers to use knowledge about their I/O to exploit that parallelism. In addition to providing an insufficient interface, most current multiprocessor file systems are optimized for a different workload than they are being asked to support. In this work we examine current multiprocessor file systems, as well as how those file systems are used by scientific applications. Contrary to the expectations of the designers of current parallel file systems, the workloads on those systems are dominated by requests to read and write small pieces of data. Furthermore, rather than being accessed sequentially and contiguously, as in uniprocessor and supercomputer workloads, files in multiprocessor file systems are accessed in regular, structured, but non-contiguous patterns. Based on our observations of multiprocessor workloads, we have designed Galley, a new parallel file system that is intended to efficiently support realistic scientific multiprocessor workloads. In this work, we introduce Galley and discuss its design and implementation. We describe Galley\u27s new three-dimensional file structure and discuss how that structure can be used by parallel applications to achieve higher performance. We introduce several new data-access interfaces, which allow applications to explicitly describe the regular access patterns we found to be common in parallel file system workloads. We show how these new interfaces allow parallel applications to achieve tremendous increases in I/O performance. Finally, we discuss how Galley\u27s new file structure and data-access interfaces can be useful in practice

GPU LSM: A Dynamic Dictionary Data Structure for the GPU

We develop a dynamic dictionary data structure for the GPU, supporting fast insertions and deletions, based on the Log Structured Merge tree (LSM). Our implementation on an NVIDIA K40c GPU has an average update (insertion or deletion) rate of 225 M elements/s, 13.5x faster than merging items into a sorted array. The GPU LSM supports the retrieval operations of lookup, count, and range query operations with an average rate of 75 M, 32 M and 23 M queries/s respectively. The trade-off for the dynamic updates is that the sorted array is almost twice as fast on retrievals. We believe that our GPU LSM is the first dynamic general-purpose dictionary data structure for the GPU.Comment: 11 pages, accepted to appear on the Proceedings of IEEE International Parallel and Distributed Processing Symposium (IPDPS'18