The Cowl - v.82 - n.12 - Dec 7, 2017

The Cowl - student newspaper of Providence College. Volume 82, Number 12 - December 7, 2017. 28 pages

Accelerating Similarly Structured Data

The failure of Dennard scaling [Bohr, 2007] and the rapid growth of data produced and consumed daily [NetApp, 2012] have made mitigating the dark silicon phenomena [Esmaeilzadeh et al., 2011] and providing fast computation for processing large volumes and expansive variety of data while consuming minimal energy the utmost important challenges for modern computer architecture. This thesis introduces the concept that grouping data structures that are previously defined in software and processing them with an accelerator can significantly improve the application performance and energy efficiency. To measure the potential performance benefits of this hypothesis, this research starts out by examining the cache impacts on accelerating commonly used data structures and its applicability to popular benchmarks. We found that accelerating similarly structured data can provide substantial benefits, however, most popular benchmark suites do not contain shared acceleration targets and therefore cannot obtain significant performance or energy improvements via a handful of accelerators. To further examine this hypothesis in an environment where the common data structures are widely used, we choose to target database application domain, using tables and columns as the similarly structured data, accelerating the processing of such data, and evaluate the performance and energy efficiency. Given that data partitioning is widely used for database applications to improve cache locality, we architect and design a streaming data partitioning accelerator to assess the feasibility of big data acceleration. The results show that we are able to achieve an order of magnitude improvement in partitioning performance and energy. To improve upon the present ad-hoc communications between accelerators and general-purpose processors [Vo et al., 2013], we also architect and evaluate a streaming framework that can be used for the data parti- tioner and other streaming accelerators alike. The streaming framework can provide at least 5 GB/s per stream per thread using software control, and is able to elegantly handle interrupts and context switches using a simple save/restore. As a final evaluation of this hypothesis, we architect a class of domain-specific database processors, or Database Processing Units (DPUs), to further improve the performance and energy efficiency of database applications. As a case study, we design and implement one DPU, called Q100, to execute industry standard analytic database queries. Despite Q100's sensitivity to communication bandwidth on-chip and off-chip, we find that the low-power configuration of Q100 is able to provide three orders of magnitude in energy efficiency over a state of the art software Database Management System (DBMS), while the high-performance configuration is able to outperform the same DBMS by 70X. Based on these experiments, we conclude that grouping similarly structured data and processing it with accelerators vastly improve application performance and energy efficiency for a given application domain. This is primarily due to the fact that creating specialized encapsulated instruction and data accesses and datapaths allows us to mitigate unnecessary data movement, take advantage of data and pipeline parallelism, and consequently provide substantial energy savings while obtaining significant performance gains

Design of a Mobile Transceiver for Precision Indoor Location

This thesis documents the design and implementation process for the next generation of the WPI Precision Personnel Location (PPL) system hardware. The driving goal of the new hardware was to support a new method of radio frequency location developed at WPI referred to as Transactional Array Reconciliation Tomography (TART). This new method is based on a time of arrival (TOA) technique as opposed to the previous Singular Value Array Reconciliation Tomography (SART), which uses time difference of arrival (TDOA). The use of a TOA method requires additional timing information and necessitates a bidirectional (transmit and receive) multicarrier transaction. The design of the new transceiver that can function as both a mobile locator and a static reference unit is the main focus of this thesis. This redesign also addressed previous hardware issues that have been exposed through extensive use in real world testing

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion