This paper shows how to generate code that efficiently converts sparse tensors between
disparate storage formats (data layouts) such as CSR, DIA, ELL, and many others. We …

Graph analytics applications often target large-scale web and social networks, which are
typically power-law graphs. Graph algorithms can often be recast as generalized Sparse …

Sparse matrix multiplication (spGEMM) is widely used to analyze the sparse network data,
and extract important information based on matrix representation. As it contains a high …

SpMV (Sparse matrix-vector multiplication) has attracted the attention of researchers in
related fields at home and abroad. Of course, improving SpMV performance has also been a …

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 …

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 …

Sparse matrix-vector multiplication (SpMV) is one of the important subroutines in numerical
linear algebras widely used in lots of large-scale applications. Accelerating SpMV on …

Vectorization seeks to accelerate computation through data-level parallelism. Vectorization
has been applied to graph processing, where the graph is traversed either in a push style or …

FPGA-based graph processing accelerators are nowadays equipped with multiple pipelines
for hardware acceleration of graph computations. However, their multi-pipeline efficiency …

We present a sparse matrix vector multiplication (SpMV) kernel that uses a novel sparse
matrix storage format and delivers superior performance for unstructured matrices on Intel …