Multiple kernel clustering (MKC) has garnered considerable attention, as their efficacy in handling nonlinear data in high-dimensional space. However, current MKC methods have three primary issues: (1) Solely focuses on clustering information while neglecting energy information and potential noise interference within the kernel; (2) The inherent manifold structure in the high-dimensional space is complex, and they lack the insufficient exploration of topological structure; (3) Most encounter cubic computational complexity, posing a formidable resource consumption challenge. To tackle the above issues, we propose a novel MKC method with shifted Laplacian on Grassmann manifold (sLGm). Firstly, sLGm constructs r-rank shifted Laplacian and subsequently reconstructs it, retaining the clustering-related and energy-related information while reducing the influence of noise. Additionally, sLGm introduces a Grassmann manifold for information fusion, which can preserve topological information in the high-dimensional space. Notably, an optimal consensus partition can be concurrently learnt from above two procedures, thereby yielding the clustering assignments, and the computational complexity of the whole procedure drops to the quadratic. Conclusively, a comprehensive suite of experiments is executed to roundly prove the effectiveness of sLGm.

Hybrid memory systems show strong potential to satisfy the growing memory demands of modern applications by combining different memory technologies. Due to the different performance characteristics of hybrid memories, a data migration strategy that migrates hot data to a faster memory is critical to the overall performance. Prior works have focused on identifying hot data and migration decisions. However, we find that the fix-sized global migration granularity in existing data migration schemes results in suboptimal performance on most workloads. The key observation is that the optimal migration granularity varies with access patterns. This paper proposes AGDM, an access-pattern-aware Adaptive Granularity Data Migration strategy for hybrid memory systems. AGDM tracks memory access patterns in runtime and accordingly adopts the most appropriate migration mode and granularity. The novel remapping-migration decoupled metadata organization enables AGDM to set local optimal gran-ularities for memory regions with different access patterns. Our evaluation shows that, compared to the state-of-the-art scheme, AGDM gets an average performance improvement of 20.06% with 29.98% energy savings.

Modern computing systems and data-intensive applications are eager for larger and faster memory. Building hybrid memory systems with different memory technologies has become a dominant trend to satisfy these demands. For hybrid memory systems, page migration schemes that dynamically migrate frequently accessed hot pages into faster memory are crucial for improving performance. However, existing migration schemes are either too aggressive, resulting in unnecessary extra traffic, or too conservative to quickly adapt to changes in access patterns. Besides, the extra latency introduced by querying metadata is often ignored or handled in an unscalable manner. In this article, we propose a relative hotness page migration (RHPM) strategy, which discovers hot pages in a set of pages by competing with each other rather than comparing with a threshold. The migration is performed only when a new page wins the competition. To overlap latency due to access metadata, RHPM fetches metadata and data in parallel. In addition, it enables a small metadata buffer to speed up metadata access. Compared to the state-of-the-art scheme, RHPM requires only 1/512 of the on-chip capacity, significantly reducing on-chip hardware overhead. Evaluation of RHPM with simulations of 25 workloads shows that RHPM outperforms the state-of-the-art scheme by an average of 13.34% in performance and saves 44.19% on energy, demonstrating better resilience to changes in access patterns.