We present a performance model-driven framework for automated performance tuning
(autotuning) of sparse matrix-vector multiply (SpMV) on systems accelerated by graphics …

Sparse matrix-vector multiplication (SpMV) is a crucial computing kernel with widespread
applications in iterative algorithms. Over the past decades, research on SpMV optimization …

Recently, graphics processing units (GPUs) have had great success in accelerating many
numerical computations. We present their application to computations on unstructured …

SpMV is a key linear algebra algorithm and has been widely used in many important
application domains. As a result, numerous attempts have been made to optimize SpMV on …

Linear systems are occurring in many applications, and solving them can take a large
amount of the total simulation time. The high performance library hypre provides a variety of …

The rising popularity of the graphics processing unit (GPU) across various numerical
computing applications triggered a breakneck race to optimize key numerical kernels and in …

Sparse matrix-vector multiplication (SpMV) is one of the important kernels of many iterative
algorithms for solving sparse linear systems. The limited storage and computational …

The sparse triangular matrix solve (SpTrSV) is an important computation kernel that is
demanded by a variety of numerical methods such as the Gauss-Seidel iterations. However …

The hybrid/heterogeneous nature of future microprocessors and large high-performance
computing systems will result in a reliance on two major types of components …

Google's famous PageRank algorithm is widely used to determine the importance of web
pages in search engines. Given the large number of web pages on the World Wide Web …