Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop cra...Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop crater detection algorithms. This paper presents a novel approach to automatically detect craters on planetary surfaces. The approach contains two parts： crater candidate region selection and crater detection. In the first part, crater candidate region selection is achieved by Kanade-Lucas-Tomasi （KLT） detector. Matrix-pattern-oriented least squares support vector machine （MatLSSVM）, as the matrixization version of least square support vector machine （SVM）, inherits the advantages of least squares support vector machine （LSSVM）, reduces storage space greatly and reserves spatial redundancies within each image matrix compared with general LSSVM. The second part of the approach employs MatLSSVM to design classifier for crater detection. Experimental results on the dataset which comprises 160 preprocessed image patches from Google Mars demonstrate that the accuracy rate of crater detection can be up to 88%. In addition, the outstanding feature of the approach introduced in this paper is that it takes resized crater candidate region as input pattern directly to finish crater detection. The results of the last experiment demonstrate that MatLSSVM-based classifier can detect crater regions effectively on the basis of KLT-based crater candidate region selection.展开更多
As one of the most essential and important operations in linear algebra, the performance prediction of sparse matrix-vector multiplication (SpMV) on GPUs has got more and more attention in recent years. In 2012, Guo a...As one of the most essential and important operations in linear algebra, the performance prediction of sparse matrix-vector multiplication (SpMV) on GPUs has got more and more attention in recent years. In 2012, Guo and Wang put forward a new idea to predict the performance of SpMV on GPUs. However, they didn’t consider the matrix structure completely, so the execution time predicted by their model tends to be inaccurate for general sparse matrix. To address this problem, we proposed two new similar models, which take into account the structure of the matrices and make the performance prediction model more accurate. In addition, we predict the execution time of SpMV for CSR-V, CSR-S, ELL and JAD sparse matrix storage formats by the new models on the CUDA platform. Our experimental results show that the accuracy of prediction by our models is 1.69 times better than Guo and Wang’s model on average for most general matrices.展开更多
稀疏线性方程组求解等高性能计算应用常常涉及稀疏矩阵向量乘(SpMV)序列Ax,A2x,…,Asx的计算.上述SpMV序列操作又称为稀疏矩阵幂函数(matrix power kernel,MPK).由于MPK执行多次SpMV且稀疏矩阵保持不变,在缓存(cache)中重用稀疏矩阵,可...稀疏线性方程组求解等高性能计算应用常常涉及稀疏矩阵向量乘(SpMV)序列Ax,A2x,…,Asx的计算.上述SpMV序列操作又称为稀疏矩阵幂函数(matrix power kernel,MPK).由于MPK执行多次SpMV且稀疏矩阵保持不变,在缓存(cache)中重用稀疏矩阵,可避免每次执行SpMV均从主存加载A,从而缓解SpMV访存受限问题,提升MPK性能.但缓存数据重用会导致相邻SpMV操作之间的数据依赖,现有MPK优化多针对单次SpMV调用,或在实现数据重用时引入过多额外开销.提出了缓存感知的MPK(cache-awareMPK,Ca-MPK),基于稀疏矩阵的依赖图,设计了体系结构感知的递归划分方法,将依赖图划分为适合缓存大小的子图/子矩阵,通过构建分割子图解耦数据依赖,根据特定顺序在子矩阵上调度执行SpMV,实现缓存数据重用.测试结果表明,Ca-MPK相对于Intel OneMKL库和最新MPK实现,平均性能提升分别多达约1.57倍和1.40倍.展开更多
To enhance network security,this study employs a deep graph matching model for vulnerability similarity detection.The model utilizes a Word Embedding layer to vectorize data words,an Image Embedding layer to vectorize...To enhance network security,this study employs a deep graph matching model for vulnerability similarity detection.The model utilizes a Word Embedding layer to vectorize data words,an Image Embedding layer to vectorize data graphs,and an LSTM layer to extract the associations between word and graph vectors.A Dropout layer is applied to randomly deactivate neurons in the LSTM layer,while a Softmax layer maps the LSTM analysis results.Finally,a fully connected layer outputs the detection results with a dimension of 1.Experimental results demonstrate that the AUC of the deep graph matching vulnerability similarity detection model is 0.9721,indicating good stability.The similarity scores for vulnerabilities such as memory leaks,buffer overflows,and targeted attacks are close to 1,showing significant similarity.In contrast,the similarity scores for vulnerabilities like out-of-bounds memory access and logical design flaws are less than 0.4,indicating good similarity detection performance.The model’s evaluation metrics are all above 97%,with high detection accuracy,which is beneficial for improving network security.展开更多
基金co-supported by the National Natural Science Foundation of China (No. 61203170)the Fundamental Research Funds for the Central Universities (No. NS2012026)Startup Foundation for Introduced Talents of Nanjing University of Aeronautics and Astronautics (No. 1007-YAH10047)
文摘Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop crater detection algorithms. This paper presents a novel approach to automatically detect craters on planetary surfaces. The approach contains two parts： crater candidate region selection and crater detection. In the first part, crater candidate region selection is achieved by Kanade-Lucas-Tomasi （KLT） detector. Matrix-pattern-oriented least squares support vector machine （MatLSSVM）, as the matrixization version of least square support vector machine （SVM）, inherits the advantages of least squares support vector machine （LSSVM）, reduces storage space greatly and reserves spatial redundancies within each image matrix compared with general LSSVM. The second part of the approach employs MatLSSVM to design classifier for crater detection. Experimental results on the dataset which comprises 160 preprocessed image patches from Google Mars demonstrate that the accuracy rate of crater detection can be up to 88%. In addition, the outstanding feature of the approach introduced in this paper is that it takes resized crater candidate region as input pattern directly to finish crater detection. The results of the last experiment demonstrate that MatLSSVM-based classifier can detect crater regions effectively on the basis of KLT-based crater candidate region selection.
文摘As one of the most essential and important operations in linear algebra, the performance prediction of sparse matrix-vector multiplication (SpMV) on GPUs has got more and more attention in recent years. In 2012, Guo and Wang put forward a new idea to predict the performance of SpMV on GPUs. However, they didn’t consider the matrix structure completely, so the execution time predicted by their model tends to be inaccurate for general sparse matrix. To address this problem, we proposed two new similar models, which take into account the structure of the matrices and make the performance prediction model more accurate. In addition, we predict the execution time of SpMV for CSR-V, CSR-S, ELL and JAD sparse matrix storage formats by the new models on the CUDA platform. Our experimental results show that the accuracy of prediction by our models is 1.69 times better than Guo and Wang’s model on average for most general matrices.
文摘稀疏线性方程组求解等高性能计算应用常常涉及稀疏矩阵向量乘(SpMV)序列Ax,A2x,…,Asx的计算.上述SpMV序列操作又称为稀疏矩阵幂函数(matrix power kernel,MPK).由于MPK执行多次SpMV且稀疏矩阵保持不变,在缓存(cache)中重用稀疏矩阵,可避免每次执行SpMV均从主存加载A,从而缓解SpMV访存受限问题,提升MPK性能.但缓存数据重用会导致相邻SpMV操作之间的数据依赖,现有MPK优化多针对单次SpMV调用,或在实现数据重用时引入过多额外开销.提出了缓存感知的MPK(cache-awareMPK,Ca-MPK),基于稀疏矩阵的依赖图,设计了体系结构感知的递归划分方法,将依赖图划分为适合缓存大小的子图/子矩阵,通过构建分割子图解耦数据依赖,根据特定顺序在子矩阵上调度执行SpMV,实现缓存数据重用.测试结果表明,Ca-MPK相对于Intel OneMKL库和最新MPK实现,平均性能提升分别多达约1.57倍和1.40倍.
基金Special Project Funded by Tsinghua University Press:“Engineering Drawing and CAD”Course Construction and Textbook Development。
文摘To enhance network security,this study employs a deep graph matching model for vulnerability similarity detection.The model utilizes a Word Embedding layer to vectorize data words,an Image Embedding layer to vectorize data graphs,and an LSTM layer to extract the associations between word and graph vectors.A Dropout layer is applied to randomly deactivate neurons in the LSTM layer,while a Softmax layer maps the LSTM analysis results.Finally,a fully connected layer outputs the detection results with a dimension of 1.Experimental results demonstrate that the AUC of the deep graph matching vulnerability similarity detection model is 0.9721,indicating good stability.The similarity scores for vulnerabilities such as memory leaks,buffer overflows,and targeted attacks are close to 1,showing significant similarity.In contrast,the similarity scores for vulnerabilities like out-of-bounds memory access and logical design flaws are less than 0.4,indicating good similarity detection performance.The model’s evaluation metrics are all above 97%,with high detection accuracy,which is beneficial for improving network security.
