Sparse linear algebra includes the fundamental and important operations in various large-
scale scientific computing and real-world applications. There exists performance bottleneck …

Tensor algebra is widely used in many applications, such as scientific computing, machine
learning, and data analytics. The tensors represented real-world data are usually large and …

Sparse matrix-sparse vector (SpMSpV) multiplication is one of the fundamental and
important operations in many high-performance scientific and engineering applications. The …

The traditional malware research is mainly based on its recognition and detection as a
breakthrough point, without focusing on its propagation trends or predicting the …

Molecular dynamics (MD) is one of the most crucial computer simulation methods for
understanding real-world processes at the atomic level. Reactive potentials based on the …

Modular exponentiation, an operation widely utilized in cryptographic protocols to transfer
text and other forms of data, can also be applied to Internet-of-Things (IoT) devices with high …

Network-on-chip-based heterogeneous multiprocessor system-on-a chip (NoC-HMPSoC) a
single board computer is extensively utilized in many real-time applications such as mobile …

Sparse Tensor-Times-Matrix (SpTTM) is the core calculation in tensor analysis. The sparse
distributions of different tensors vary greatly, which poses a big challenge to designing …

Canonical Polyadic Decomposition (CPD) of sparse tensors is an effective tool in various
machine learning and data analytics applications, in which sparse Matricized Tensor Times …

Purpose Sorting is a very important algorithm to solve problems in computer science. The
most well-known divide and conquer sorting algorithm is quicksort. It starts with dividing the …