Automated performance modeling and performance prediction of parallel programs are
highly valuable in many use cases, such as in guiding task management and job …

Graphics processing units (GPUs) have delivered a remarkable performance for a variety of
high performance computing (HPC) applications through massive parallelism. One such …

Sparse matrix–vector multiplication (SpMV) is one of the most indispensable kernels of
solving problems in numerous applications, but its performance of SpMV is limited by the …

Despite numerous efforts for optimizing the performance of Sparse Matrix and Vector
Multiplication (SpMV) on modern hardware architectures, few works are done to its sparse …

Canonical polyadic decomposition (CPD) is one of the most common tensor computations
adopted in many scientific applications. The major bottleneck of CPD is matricized tensor …

In this paper, we implement and evaluate a one-sided communication-based distributed-
memory sparse triangular solve (SpTRSV). SpTRSV is used in conjunction with Sparse LU …

This paper describes REAP, a software-hardware approach that enables high performance
sparse linear algebra computations on a cooperative CPU-FPGA platform. REAP carefully …

Sparse matrix-vector multiplication (SpMV) is an important kernel in many scientific, machine-
learning, and other compute-intensive applications. Performance characteristics, however …

Sparse matrix is any matrix with enough zeros that it pays to take advantage of them. The
computational efficiency of sparse matrix–vector multiplication (SpMV) is significantly …

Tucker decomposition is one of the most popular models for analyzing and compressing
large-scale tensorial data. Existing Tucker decomposition algorithms usually rely on a single …