In modern computing, the persistent issue of the memory wall significantly inhibits performance efficiency, especially in Single Instruction, Multiple Data (SIMD) architectures processing data-parallel workloads. Contemporary applications, including machine learning, data analytics, and computer vision, often feature complex chains of dependent, indirect, stride, and indexed vector memory accesses. These intricate access patterns place substantial pressure on vector memory units, leading to underutilization of SIMD resources and overall performance degradation. This study introduces ChameSC11A combination of Chameleon and Superscalar Core., a novel, adaptive SIMD architecture addressing the escalating memory wall issue. ChameSC exploits the typically idle memory units of the superscalar core during data-parallel workload execution by dynamically virtualizing the core to function as a precise data prefetcher, fetching data exactly as needed by the SIMD architecture in advance. The L1 cache of the superscalar core serves as a proactive cache to store prefetched data, which can be directly accessed by the vector memory unit. This strategic utilization of idle resources significantly enhances memory-level parallelism and effective memory bandwidth. Evaluations of ChameSC demonstrate substantial performance enhancements, achieving an average speedup of 34.3% compared to conventional SIMD architectures across a range of representative data-parallel applications with various memory access behaviors. Compared to state-of-the-art data prefetching techniques such as Berti and IPCP, ChameSC offers better performance without introducing extra storage overhead.

Multi-view clustering, which appropriately integrates information from multiple sources to reveal data’s inherent structure, is gaining traction in clustering. Though existing procedures have yielded satisfactory results, we observe that they have neglected the inherent local structure in the base kernels. This may cause adverse effects on clustering. To solve the problem, we introduce LF-MKC-LKA, a simple yet effective late fusion multiple kernel clustering with local kernel alignment maximisation approach. In particular, we first determine the nearest$k$neighbours in the average kernel space for each sample and record the information in the nearest neighbor indicator matrix. Then, the nearest neighbor indicator matrix can be used to generate local structure matrix of each sample. The local kernels of each view may then be generated using the local structure matrix, retaining just the highly confident local similarities for learning the intrinsic global manifold of data. They can also be utilised to keep the block diagonal structure and improve the robustness of the underlying kernels against noise.We input the local kernels of each view into the kernel$k$-means (KKM) algorithm and get the local base partitions. Finally, we use a three-step iterative optimization approach to maximize the alignment of the consensus partition using base partitions and a regularisation term. As demonstrated, a significant number of trials on 11 multi-kernel benchmark datasets have shown that the proposed LF-MKC-LKA is effective and efficient. A number of experiments are also designed to demonstrate the fast convergence, excellent performance, robustness and low parameter sensitivity of the algorithm. Our code can be find athttps://github.com/TiejianZhang/TMM21-LF-MKC-LKA.

Anchor enhanced multi-view late fusion clustering has attracted numerous researchers' attention for its high clustering accuracy and promising efficiency. However, in the existing methods, the anchor points are usually generated through sampling or linearly combining the samples within the datasets, which could result in enormous time consumption and limited representation capability. To solve the problem, in our method, we learn the view-specific anchor points by learning them directly. Specifically, in our method, we first reconstruct the partition matrix of each view through multiplying a view-specific anchor matrix by a consensus reconstruction matrix. Then, by maximizing the weighted alignment between the base partition matrix and its estimated version in each view, we learn the optimal anchor points for each view. In particular, unlike previous late fusion algorithms, which define anchor points as linear combinations of existing samples, we define anchor points as a series of orthogonal vectors that are directly learned through optimization, which expands the learning space of the anchor points. Moreover, based on the above design, the resultant algorithm has only linear complexity and no hyper-parameter. Experiments on $12$ benchmark kernel datasets and 5 large-scale datasets illustrate that the proposed Efficient Anchor Learning-based Multi-view Clustering (AL-MVC) algorithm achieves the state-of-the-art performance in both clustering performance and efficiency.