Sliding Block Hashing (Slick) -- Basic Algorithmic Ideas

We present {\bf Sli}ding Blo{\bf ck} Hashing (Slick), a simple hash table data structure that combines high performance with very good space efficiency. This preliminary report outlines avenues for analysis and implementation that we intend to pursue

Performance Comparison of Linear Hashing and Extendible Hashing

Computing and Information Scienc

Incremental file reorganization schemes

Issued as Final project report, Project no. G-36-66

Forecasting the cost of processing multi-join queries via hashing for main-memory databases (Extended version)

Database management systems (DBMSs) carefully optimize complex multi-join queries to avoid expensive disk I/O. As servers today feature tens or hundreds of gigabytes of RAM, a significant fraction of many analytic databases becomes memory-resident. Even after careful tuning for an in-memory environment, a linear disk I/O model such as the one implemented in PostgreSQL may make query response time predictions that are up to 2X slower than the optimal multi-join query plan over memory-resident data. This paper introduces a memory I/O cost model to identify good evaluation strategies for complex query plans with multiple hash-based equi-joins over memory-resident data. The proposed cost model is carefully validated for accuracy using three different systems, including an Amazon EC2 instance, to control for hardware-specific differences. Prior work in parallel query evaluation has advocated right-deep and bushy trees for multi-join queries due to their greater parallelization and pipelining potential. A surprising finding is that the conventional wisdom from shared-nothing disk-based systems does not directly apply to the modern shared-everything memory hierarchy. As corroborated by our model, the performance gap between the optimal left-deep and right-deep query plan can grow to about 10X as the number of joins in the query increases.Comment: 15 pages, 8 figures, extended version of the paper to appear in SoCC'1

Scalable Hash Tables

The term scalability with regards to this dissertation has two meanings: It means taking the best possible advantage of the provided resources (both computational and memory resources) and it also means scaling data structures in the literal sense, i.e., growing the capacity, by “rescaling” the table. Scaling well to computational resources implies constructing the fastest best per- forming algorithms and data structures. On today’s many-core machines the best performance is immediately associated with parallelism. Since CPU frequencies have stopped growing about 10-15 years ago, parallelism is the only way to take ad- vantage of growing computational resources. But for data structures in general and hash tables in particular performance is not only linked to faster computations. The most execution time is actually spent waiting for memory. Thus optimizing data structures to reduce the amount of memory accesses or to take better advantage of the memory hierarchy especially through predictable access patterns and prefetch- ing is just as important. In terms of scaling the size of hash tables we have identified three domains where scaling hash-based data structures have been lacking previously, i.e., space effi- cient growing, concurrent hash tables, and Approximate Membership Query data structures (AMQ-filter). Throughout this dissertation, we describe the problems in these areas and develop efficient solutions. We highlight three different libraries that we have developed over the course of this dissertation, each containing mul- tiple implementations that have shown throughout our testing to be among the best implementations in their respective domains. In this composition they offer a comprehensive toolbox that can be used to solve many kinds of hashing related problems or to develop individual solutions for further ones. DySECT is a library for space efficient hash tables specifically growing space effi- cient hash tables that scale with their input size. It contains the namesake DySECT data structure in addition to a number of different probing and cuckoo based im- plementations. Growt is a library for highly efficient concurrent hash tables. It contains a very fast base table and a number of extensions to adapt this table to match any purpose. All extension can be combined to create a variety of different interfaces. In our extensive experimental evaluation, each adaptation has shown to be among the best hash tables for their specific purpose. Lpqfilter is a library for concurrent approximate membership query (AMQ) data structures. It contains some original data structures, like the linear probing quotient filter, as well as some novel approaches to dynamically sized quotient filters

Android Protection System: A Signed Code Security Mechanism for Smartphone Applications

This research develops the Android Protection System (APS), a hardware-implemented application security mechanism on Android smartphones. APS uses a hash-based white-list approach to protect mobile devices from unapproved application execution. Functional testing confirms this implementation allows approved content to execute on the mobile device while blocking unapproved content. Performance benchmarking shows system overhead during application installation increases linearly as the application package size increases. APS presents no noticeable performance degradation during application execution. The security mechanism degrades system performance only during application installation, when users expect delay. APS is implemented within the default Android application installation process. Applications are hashed prior to installation and compared against a white-list of approved content. APS allows applications that generate a matching hash; all others are blocked. APS blocks 100% of unapproved content while allowing 100% of approved content. Performance overhead for APS varies from 100.5% to 112.5% with respect to the default Android application installation process. This research directly supports the efforts of the USAF and the DoD to protect our information and ensure that adversaries do not gain access to our systems

FPGA-based architectures for next generation communications networks

This engineering doctorate concerns the application of Field Programmable Gate Array (FPGA) technology to some of the challenges faced in the design of next generation communications networks. The growth and convergence of such networks has fuelled demand for higher bandwidth systems, and a requirement to support a diverse range of payloads across the network span. The research which follows focuses on the development of FPGA-based architectures for two important paradigms in contemporary networking - Forward Error Correction and Packet Classification. The work seeks to combine analysis of the underlying algorithms and mathematical techniques which drive these applications, with an informed approach to the design of efficient FPGA-based circuits