1479535
Self Adaptive Application Level Fault Tolerance for Parallel and Distributed Computing
Most application level fault tolerance schemes in literature are non-adaptive in the sense that the fault tolerance schemes incorporated in applications are usually designed without incorporating information from system environments such as the amount of available memory and the local or network I/O bandwidth. However, from an application point of view, it is often desirable for fault tolerant high performance applications to be able to achieve high performance under whatever system environment it executes with as low fault tolerance overhead as possible In this paper, we demonstrate that, in order to achieve high reliability with as low performance penalty as possible, fault tolerant schemes in applications need to be able to adapt themselves to different system environments. We propose a framework under which different fault tolerant schemes can be incorporated in applications using an adaptive method. Under this framework, applications are able to choose near optimal fault tolerance schemes at run time according to the specific characteristics of the platform on which the application is executing.
5809396
Self-adapting numerical software (SANS) effort
The challenge for the development of next-generation software is the successful management of the complex computational environment while delivering to the scientist the full power of flexible compositions of the available algorithmic alternatives. Self-adapting numerical software (SANS) systems are intended to meet this significant challenge. The process of arriving at an efficient numerical solution of problems in computational science involves numerous decisions by a numerical expert. Attempts to automate such decisions distinguish three levels: algorithmic decision, management of the parallel environment, and processor-specific tuning of kernels. Additionally, at any of these levels we can decide to rearrange the user's data. In this paper we look at a number of efforts at the University of Tennessee to investigate these areas.
1853937
Numerically Stable Real Number Codes Based on Random Matrices
Error correction codes defined over real-number field have been studied and recognized as useful in many applications. However, most real-number codes in literature are quite suspect in their numerical stability. In this paper, we introduce a class of real-number codes based on random generator matrices over real-number fields. Codes over complex-number field are also discussed. Experiment results demonstrate our codes are numerically much more stable than existing codes in literature.
37360166
PAWS: Personalized Adaptive Web Search
34031681
Finding a Hidden Code by Asking Questions
We study the problem of finding a hidden code k in the domain {1, ..., m}n in the presence of an oracle which, for any x in the domain, answers a pair of numbers a(x,k) and b(x,k) such that a(x,k) is the number of components coinciding in x and k and, b(x, k) is the sum of a(x, k) and the number of components occurring in both x and k but, not at the same position. We show that \(\left\lceil {\frac{m}{n}} \right\rceil\)+ 2nlogn + 2n + 2 queries are sufficient to find any hidden code if m ≥ n.
3449616
Fast and memory-efficient regular expression matching for deep packet inspection
Packet content scanning at high speed has become extremely important due to its applications in network security, network monitoring, HTTP load balancing, etc. In content scanning, the packet payload is compared against a set of patterns specified as regular expressions. In this paper, we first show that memory requirements using traditional methods are prohibitively high for many patterns used in packet scanning applications. We then propose regular expression rewrite techniques that can effectively reduce memory usage. Further, we develop a grouping scheme that can strategically compile a set of regular expressions into several engines, resulting in remarkable improvement of regular expression matching speed without much increase in memory usage. We implement a new DFA-based packet scanner using the above techniques. Our experimental results using real-world traffic and patterns show that our implementation achieves a factor of 12 to 42 performance improvement over a commonly used DFA- based scanner. Compared to the state-of-art NFA-based implementation, our DFA-based packet scanner achieves 50 to 700 times speedup.
5899080
Optimizing Web Search Using Spreading Activation on the Clickthrough Data
In this paper, we propose a mining algorithm to utilize the user click-through data to improve search performance. The algorithm first explores the relationship between queries and Web pages and mine out co-visiting relationship as the virtual link among the Web pages, and then Spreading Activation mechanism is used to perform the query-dependent search. Our approach could overcome the challenges discussed above and the experimental results on a large set of MSN click-through log data show a significant improvement on search performance over the DirectHit algorithm as well as the baseline search engine.
16533233
Improving text classification using local latent semantic indexing
Latent semantic indexing (LSI) has been shown to be extremely useful in information retrieval, but it is not an optimal representation for text classification. It always drops the text classification performance when being applied to the whole training set (global LSI) because this completely unsupervised method ignores class discrimination while only concentrating on representation. Some local LSI methods have been proposed to improve the classification by utilizing class discrimination information. However, their performance improvements over original term vectors are still very limited. In this paper, we propose a new local LSI method called "local relevancy weighted LSI" to improve text classification by performing a separate single value decomposition (SVD) on the transformed local region of each class. Experimental results show that our method is much better than global LSI and traditional local LSI methods on classification within a much smaller LSI dimension.