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

Shared Arrangements: practical inter-query sharing for streaming dataflows

Current systems for data-parallel, incremental processing and view maintenance over high-rate streams isolate the execution of independent queries. This creates unwanted redundancy and overhead in the presence of concurrent incrementally maintained queries: each query must independently maintain the same indexed state over the same input streams, and new queries must build this state from scratch before they can begin to emit their first results. This paper introduces shared arrangements: indexed views of maintained state that allow concurrent queries to reuse the same in-memory state without compromising data-parallel performance and scaling. We implement shared arrangements in a modern stream processor and show order-of-magnitude improvements in query response time and resource consumption for interactive queries against high-throughput streams, while also significantly improving performance in other domains including business analytics, graph processing, and program analysis

Optimal Hierarchical Layouts for Cache-Oblivious Search Trees

This paper proposes a general framework for generating cache-oblivious layouts for binary search trees. A cache-oblivious layout attempts to minimize cache misses on any hierarchical memory, independent of the number of memory levels and attributes at each level such as cache size, line size, and replacement policy. Recursively partitioning a tree into contiguous subtrees and prescribing an ordering amongst the subtrees, Hierarchical Layouts generalize many commonly used layouts for trees such as in-order, pre-order and breadth-first. They also generalize the various flavors of the van Emde Boas layout, which have previously been used as cache-oblivious layouts. Hierarchical Layouts thus unify all previous attempts at deriving layouts for search trees. The paper then derives a new locality measure (the Weighted Edge Product) that mimics the probability of cache misses at multiple levels, and shows that layouts that reduce this measure perform better. We analyze the various degrees of freedom in the construction of Hierarchical Layouts, and investigate the relative effect of each of these decisions in the construction of cache-oblivious layouts. Optimizing the Weighted Edge Product for complete binary search trees, we introduce the MinWEP layout, and show that it outperforms previously used cache-oblivious layouts by almost 20%.Comment: Extended version with proofs added to the appendi

The Logarithmic Funnel Heap: A Statistically Self-Similar Priority Queue

The present work contains the design and analysis of a statistically self-similar data structure using linear space and supporting the operations, insert, search, remove, increase-key and decrease-key for a deterministic priority queue in expected O(1) time. Extract-max runs in O(log N) time. The depth of the data structure is at most log* N. On the highest level, each element acts as the entrance of a discrete, log* N-level funnel with a logarithmically decreasing stem diameter, where the stem diameter denotes a metric for the expected number of items maintained on a given level.Comment: 14 pages, 4 figure

MDS-WLAN: Maximal Data Security in WLAN for Resisting Potential Threats

The utmost security standards over Wireless Local Area Network (WLAN) are still an unsolved answer in research community as well as among the commercial users. There are various prior attempts in proposing security of WLAN that lacks focus on access point and is found to be quite complex implementation of cryptography. The proposed paper presents a novel, simple, and yet robust technique called as MDS-WLAN i.e. maximal data security in WLAN. The system is evaluated over laboratory prototype and mitigation measures are drawn for resisting wormhole attack, Sybil attack, and rogue access point issue in WLAN. The outcome of the MDS is compared with conventional AES and SHA that shows optimal communication performance and highest data security