東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Parallel Algorithms for Medical Info...

Moazeni, Maryam.

Linked to FindBook

Google Book

Amazon

博客來

Parallel Algorithms for Medical Informatics on Data-Parallel Many-Core Processors.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Parallel Algorithms for Medical Informatics on Data-Parallel Many-Core Processors./
Author:	Moazeni, Maryam.
Description:	155 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
Contained By:	Dissertation Abstracts International74-10B(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3564389
ISBN:	9781303132223

Parallel Algorithms for Medical Informatics on Data-Parallel Many-Core Processors.
Moazeni, Maryam.

Parallel Algorithms for Medical Informatics on Data-Parallel Many-Core Processors. - 155 p.

Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2013.

The extensive use of medical monitoring devices has resulted in the generation of tremendous amounts of data. Storage, retrieval, and analysis of such data require platforms that can scale with data growth and adapt to the various behavior of the analysis and processing algorithms. In recent years, many-core processors and more specifically many-core Graphical Processing Units (GPUs) have become one of the most promising platforms for high performance processing of data, due to the massive parallel processing power they offer. However, many of the algorithms and data structures used in medical and bioinformatics systems do not follow a data-parallel programming paradigm, and hence cannot fully benefit from the parallel processing power of data-parallel many-core architectures.

ISBN: 9781303132223Subjects--Topical Terms:

626642
Computer Science.

Parallel Algorithms for Medical Informatics on Data-Parallel Many-Core Processors.
LDR:04429nam a2200325 4500 001 1967863
005 20141121132932.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303132223
035 $a (MiAaPQ)AAI3564389
035 $a AAI3564389
040 $a MiAaPQ $c MiAaPQ
100 1 $a Moazeni, Maryam. $3 2104954
245 1 0 $a Parallel Algorithms for Medical Informatics on Data-Parallel Many-Core Processors.
300 $a 155 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
500 $a Adviser: Majid Sarrafzadeh.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2013.
520 $a The extensive use of medical monitoring devices has resulted in the generation of tremendous amounts of data. Storage, retrieval, and analysis of such data require platforms that can scale with data growth and adapt to the various behavior of the analysis and processing algorithms. In recent years, many-core processors and more specifically many-core Graphical Processing Units (GPUs) have become one of the most promising platforms for high performance processing of data, due to the massive parallel processing power they offer. However, many of the algorithms and data structures used in medical and bioinformatics systems do not follow a data-parallel programming paradigm, and hence cannot fully benefit from the parallel processing power of data-parallel many-core architectures.
520 $a In this dissertation, we present three techniques to adapt several non-data parallel applications in different dwarfs to modern many-core GPUs. First, we present a load balancing technique to maximize parallelism in non-serial polyadic Dynamic Programming (DP), which is a family of dynamic programming algorithms with more non-uniform data access pattern. We show that a bottom-up approach to solving the DP problem exploits more parallelism and therefore yields higher performance. We achieve 228X speedup over an equivalent CPU implementation.
520 $a Second, we introduce a parallel hash table as a parallel-friendly lock-free dynamic hash table. The parallel hash table structure reduces the contention on the shared objects in lock-free hash table and achieves significant throughput on many-core processor architectures. To reduce the contention, it creates multiple instances of a hash table and uses a table assignment function to distribute hash table operations to different hash table instances and guarantees key uniqueness. We achieved roughly 27X speedup over counter-part multi-thread lock-free hash table on CPU.
520 $a Third, we present a memory optimization technique for the software-managed scratchpad memory based on G80, GT200, and Fermi architectures to alleviate the constraints of using scratchpad memory. We propose a memory optimization scheme that minimizes the usage of memory space by discovering the chances of memory reuse with the goal of maximizing application performance. Our solution is based on graph coloring. Our evaluations show that using this technique can reduce the execution time of applications on GPUs by up to 22% over the non-optimized GPU implementation.
520 $a In addition, by leveraging massive parallelism of GPUs, we introduce a novel time-series searching technique for multi-dimensional time series. Searching for time series is an intuitive and practical approach to study similarity of patterns, events, and activities in patient histories. However, its computational intensity has traditionally been a constraint in the development of a complex algorithm that can handle patterns in multi-dimensional signals considering noise, scaling, and time correlation between dimensions. Using GPUs, we are able to achieve high speed up in processing signals, while improving the quality of the search algorithm and tackle problems such as noise and scaling. We used data collected from two medical monitoring devices, a Personal Activity Monitor (PAM) and Medical Shoe to evaluate our approach and show that our technique results in up to 25X speed up and up to 15 point improvement in Normalized Discounted Cumulative Gain (NDCG) for such application.
590 $a School code: 0031.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Computer. $3 1669061
690 $a 0984
690 $a 0464
710 2 $a University of California, Los Angeles. $b Computer Science 0201. $3 2049859
773 0 $t Dissertation Abstracts International $g 74-10B(E).
790 $a 0031
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3564389