skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Assembly Algorithms for PDEs with Uncertain Input Data on Emerging Multicore Architectures.


Abstract not provided.

Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the 2015 SIAM Conference on Computational Science and Engineering held March 13-18, 2015 in Salt Lake City, UT.
Country of Publication:
United States

Citation Formats

Phipps, Eric Todd, and Edwards, Harold C. Assembly Algorithms for PDEs with Uncertain Input Data on Emerging Multicore Architectures.. United States: N. p., 2015. Web.
Phipps, Eric Todd, & Edwards, Harold C. Assembly Algorithms for PDEs with Uncertain Input Data on Emerging Multicore Architectures.. United States.
Phipps, Eric Todd, and Edwards, Harold C. 2015. "Assembly Algorithms for PDEs with Uncertain Input Data on Emerging Multicore Architectures.". United States. doi:.
title = {Assembly Algorithms for PDEs with Uncertain Input Data on Emerging Multicore Architectures.},
author = {Phipps, Eric Todd and Edwards, Harold C.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2015,
month = 3

Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • Abstract not provided.
  • Abstract not provided.
  • String matching algorithms are critical to several scientific fields. Beside text processing and databases, emerging applications such as DNA protein sequence analysis, data mining, information security software, antivirus, ma- chine learning, all exploit string matching algorithms [3]. All these applica- tions usually process large quantity of textual data, require high performance and/or predictable execution times. Among all the string matching algorithms, one of the most studied, especially for text processing and security applica- tions, is the Aho-Corasick algorithm. 1 2 Book title goes here Aho-Corasick is an exact, multi-pattern string matching algorithm which performs the search in a time linearlymore » proportional to the length of the input text independently from pattern set size. However, depending on the imple- mentation, when the number of patterns increase, the memory occupation may raise drastically. In turn, this can lead to significant variability in the performance, due to the memory access times and the caching effects. This is a significant concern for many mission critical applications and modern high performance architectures. For example, security applications such as Network Intrusion Detection Systems (NIDS), must be able to scan network traffic against very large dictionaries in real time. Modern Ethernet links reach up to 10 Gbps, and malicious threats are already well over 1 million, and expo- nentially growing [28]. When performing the search, a NIDS should not slow down the network, or let network packets pass unchecked. Nevertheless, on the current state-of-the-art cache based processors, there may be a large per- formance variability when dealing with big dictionaries and inputs that have different frequencies of matching patterns. In particular, when few patterns are matched and they are all in the cache, the procedure is fast. Instead, when they are not in the cache, often because many patterns are matched and the caches are continuously thrashed, they should be retrieved from the system memory and the procedure is slowed down by the increased latency. Efficient implementations of string matching algorithms have been the fo- cus of several works, targeting Field Programmable Gate Arrays [4, 25, 15, 5], highly multi-threaded solutions like the Cray XMT [34], multicore proces- sors [19] or heterogeneous processors like the Cell Broadband Engine [35, 22]. Recently, several researchers have also started to investigate the use Graphic Processing Units (GPUs) for string matching algorithms in security applica- tions [20, 10, 32, 33]. Most of these approaches mainly focus on reaching high peak performance, or try to optimize the memory occupation, rather than looking at performance stability. However, hardware solutions supports only small dictionary sizes due to lack of memory and are difficult to customize, while platforms such as the Cell/B.E. are very complex to program.« less