随着片上多处理器系统核数的增加,当前一致性协议上存在的许多问题使共享存储系统复杂而低效.目前一些一致性协议极其复杂,例如MESI(modified exclusive shared or invalid)协议,存在众多的中间状态和竞争.并且这些协议还会导致额外失...随着片上多处理器系统核数的增加,当前一致性协议上存在的许多问题使共享存储系统复杂而低效.目前一些一致性协议极其复杂,例如MESI(modified exclusive shared or invalid)协议,存在众多的中间状态和竞争.并且这些协议还会导致额外失效通信,以及大量记录共享信息的目录存储开销(目录协议)或广播消息的网络开销(监听协议).对数据无竞争的程序实现了一种简单高效一致性协议VISU(valid/invalid states based on self-updating),这种协议基于自更新操作(self-updating)、只包含2个稳定状态(valid/invalid).所设计的两状态VISU协议消除了目录和间接事务.首先基于并行编程的数据无竞争(data race free, DRF)模型,采用在同步点进行自更新共享数据来保证正确性.其次利用动态识别私有和共享数据的技术,提出了对私有数据进行写回、对共享数据进行写直达的方案.对于私有数据,简单的写回策略能够简化不必要的片上通信.在L1 cache中,对于共享数据的写直达方式能确保LLC(last level cache)中数据最新从而消除了几乎所有的一致性状态.实现的VISU协议开销低、不需要目录、没有间接传输和众多的一致性状态,且更加容易验证,同时获得了与MESI目录协议几乎相当甚至更优的性能.展开更多
Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in...Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.展开更多
文摘随着片上多处理器系统核数的增加,当前一致性协议上存在的许多问题使共享存储系统复杂而低效.目前一些一致性协议极其复杂,例如MESI(modified exclusive shared or invalid)协议,存在众多的中间状态和竞争.并且这些协议还会导致额外失效通信,以及大量记录共享信息的目录存储开销(目录协议)或广播消息的网络开销(监听协议).对数据无竞争的程序实现了一种简单高效一致性协议VISU(valid/invalid states based on self-updating),这种协议基于自更新操作(self-updating)、只包含2个稳定状态(valid/invalid).所设计的两状态VISU协议消除了目录和间接事务.首先基于并行编程的数据无竞争(data race free, DRF)模型,采用在同步点进行自更新共享数据来保证正确性.其次利用动态识别私有和共享数据的技术,提出了对私有数据进行写回、对共享数据进行写直达的方案.对于私有数据,简单的写回策略能够简化不必要的片上通信.在L1 cache中,对于共享数据的写直达方式能确保LLC(last level cache)中数据最新从而消除了几乎所有的一致性状态.实现的VISU协议开销低、不需要目录、没有间接传输和众多的一致性状态,且更加容易验证,同时获得了与MESI目录协议几乎相当甚至更优的性能.
文摘Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.
