This paper investigates suitable indexing techniques to enable efficient content-based audio retrieval in large acoustic databases. To make an index-based retrieval mechanism applicable to audio content, we investigate the design of Locality Sensitive Hashing (LSH) and the partial sequence comparison. We propose a fast and efficient audio retrieval framework of query-by-content and develop an audio retrieval system. Based on this framework, four different audio retrieval schemes, LSH-Dynamic Programming (DP), LSH-Sparse DP (SDP), Exact Euclidian LSH (E2LSH)-DP, E2LSH-SDP, are introduced and evaluated in order to better understand the performance of audio retrieval algorithms. The experimental results indicate that compared with the traditional DP and the other three compititive schemes, E2LSH-SDP exhibits the best tradeoff in terms of the response time, retrieval accuracy and computation cost.

In this paper, we propose an analytic model using a semi-markov process for parallel computers which provides hardware support for a cache coherence mechanism. The model proposed here, the Semi-markov Memory and Cache coherence Interference model, can be used for the performance prediction of cache coherence based parallel computers since it can be easily applied to descriptions of the waiting states due to network contention or memory interference of both normal data accesses and cache coherence requests. Conventional analytic models using stochastic processes to describe parallel computers have the problem of numerical explosion in the number of states necessary as the system size increases even for simple parallel computers without cache coherence mechanisms. The number of states required by constructing our proposing analytic model, however, does not depend on the system size but only on the kind of cache coherence protocol. For example, the number of states for the Synapse cache coherence protocol is only 20, as is described in this paper. Using the proposed analytic model, we investigate several comparative experiments with widely known simulation results. We found that there is only a 7.08% difference between the simulation and our analytic model, while our analytic model can predict the performance of a 1,024 processor system in the order of microseconds.

In this paper, we apply the Semi-markov Memory and Cache coherence Interference (SMCI) model, which we had proposed for invalidating based cache coherent parallel computers, to an updating based protocol. The model proposed here, the SMCI/Dragon model, can predict performance of cache coherent parallel computers with the Dragon protocol as well as the original SMCI model for the Synapse protocol. Conventional analytic models by stochastic processes to describe parallel computers have the problem of numerical explosion in the number of states necessary as the system size increases. We have already shown that the SMCI model achieved both the small number of states to describe parallel computers with the Synapse protocol and the inexpensive computation cost to predict their performance. In this paper, we demonstrate generality of the SMCI model by applying it to the another cache coherence protocol, Dragon, which has opposite characteristics than Synapse. We show the number of states required by constructing the SMCI/Dragon model is only 21 which is as small as SMCI/Synapse, and the computation cost is also the order of microseconds. Using the SMCI/Dragon model, we investigate several comparative experiments with widely known simulation results. We found that there is only a 5. 4% differences between the simulation and the SMCI/Dragon model.