The hashing method is widely used as the index structure, which can be stored in NVM to improve the application performance. However, existing hashing methods may cause high extra write amplification to NVM and bring high additional storage overhead on NVM while providing low request performance. To solve these problems, we have proposed a dynamic hashing method calledHierarchical Hashing, whose basic idea is to leverage a novel hash collision resolution mechanism that can dynamically expand the size of the hash table.Hierarchical Hashingcan incur no extra write amplification to NVM when resolving hash collisions. Additionally, it can directly address all cells when resizing the hash table, thereby avoiding the additional storage overhead caused by non-addressable linked lists. Furthermore, the request performance can be improved as all cells of the hash table are addressable when resizing to resolve hash collisions. The experimental results demonstrate thatHierarchical Hashingbrings no extra write amplification to NVM and achieves nearly 90% space utilization and high request performance while providing 99% memory utilization, compared with existing representative hashing methods.

Summary Exploring the associations between microRNAs (miRNAs) and diseases can identify potential disease features. Prediction of miRNA‐skin cancer associations has become an effective method for detecting skin cancer, as direct detection of skin cancer is challenging. However, traditional binary associations prediction methods overlook high‐order feature differences of miRNAs in different types of skin cancer. Although current tensor decomposition methods have addressed this, they assume a consistent composition standard of global tensor elements, ignoring differences in the composition standards of local sub‐tensors. This leads to poor prediction accuracy and comprehensiveness and fails to consider the credibility and diversity requirements of patients and doctors in practical applications. In this paper, we represent miRNA‐skin cancer associations as a tensor and employ tensor decomposition for tensor completion to achieve prediction purposes. First, we propose a credibility evaluation indicator and introduce four objective functions: accuracy, comprehensiveness, credibility, and diversity, to construct a many‐objective local tensor factorization model (MOLTF). Then, to avoid wrong individuals when solving this model with genetic algorithms, we propose a corrected single‐point crossover operator and a corrected multi‐point mutation operator. On the miRNA‐disease dataset HMDD v3.2, our algorithm improves accuracy by 14.5% compared to the recent baseline, demonstrating its effectiveness.

Zero-knowledge proof (ZKP) is a popular cryptographic strategy for building a trusted environment, which can be applied to blockchain, electronic voting, and other scenarios. However, ZKP involves a number of computationally intensive operations that limit its widespread adoption in time-sensitive practical applications. The multi-scalar multiplication (MSM) dominates the computations and takes over 70% of the total computation time. This paper proposes a GPU-based acceleration method for ZKP by designing several optimization techniques for MSM. First, this paper constructs a formal mathematical formula of the Pippenger algorithm, which provides a theoretical optimization framework for MSM. Second, by parallelizing the prefix sum, the time complexity of the bucket reduction part of MSM is reduced from $\mathcal{O}\left( {3 \times {2^C}} \right)$ to $\mathcal{O}\left( {2 \times {2^C}} \right)$. Finally, this paper also analyzes the influence of group size on the final calculation time under different data scales and gives a suitable range of group sizes. Compared to the state-of-the-art method, our method can achieve 1.01× to 1.12× for throughput.