In the past decade, Graphics Processing Units have played an important role in the field of
high-performance computing and they still advance new fields such as IoT, autonomous …

Deep learning systems have been successfully applied to Euclidean data such as images,
video, and audio. In many applications, however, information and their relationships are …

Sparse matrix-vector multiplication (SpMV) is a fundamental building block for numerous
applications. In this paper, we propose CSR5 (Compressed Sparse Row 5), a new storage …

Sparse matrix-vector multiplication (SpMV) is an important primitive across a wide range of
application domains such as scientific computing and graph analytics. Due to its intrinsic …

Tiling is a key technique for data locality optimization and is widely used in high-
performance implementations of dense matrix-matrix multiplication for multicore/manycore …

Graphics Processing Units (GPUs) have numerous configuration and design options,
including core frequency, number of parallel compute units (CUs), and available memory …

Important workloads, such as machine learning and graph analytics applications, heavily
involve sparse linear algebra operations. These operations use sparse matrix compression …

The multiplication of a sparse matrix by a dense vector (SpMV) is a centerpiece of scientific
computing applications: it is the essential kernel for the solution of sparse linear systems and …

While systolic arrays are widely used for dense-matrix operations, they are seldom used for
sparse-matrix operations. In this paper, we show how a systolic array of Multiply-and …

We present a strictly balanced method for the parallel computation of sparse matrix-vector
products (SpMV). Our algorithm operates directly upon the Compressed Sparse Row (CSR) …