GPU computing performance analysis on matrix multiplication

High-Performance Matrix-Vector Multiplication on the GPU

In this paper, we develop a high-performance GPU kernel for one of the most popular dense linear algebra operations, the matrixvector multiplication. The target hardware is the most recent Nvidia Tesla 20-series (Fermi architecture), which is designed from the ground up for scientific computing. We show that it is essentially a matter of fully utilizing the fine-grained parallelism of the many-...

Sparse Matrix-Vector Multiplication on a GPU

Sparse Matrix-vector Multiplication on Nvidia Gpu

In this paper, we present our work on developing a new matrix format and a new sparse matrix-vector multiplication algorithm. The matrix format is HEC, which is a hybrid format. This matrix format is efficient for sparse matrix-vector multiplication and is friendly to preconditioner. Numerical experiments show that our sparse matrix-vector multiplication algorithm is efficient on

Fast sparse matrix multiplication on GPU

Sparse matrix multiplication is an important algorithm in a wide variety of problems, including graph algorithms, simulations and linear solving to name a few. Yet, there are but a few works related to acceleration of sparse matrix multiplication on a GPU. We present a fast, novel algorithm for sparse matrix multiplication, outperforming the previous algorithm on GPU up to 3× and CPU up to 30×....

Performance Analysis of GPU Memory Architectures with Standard Matrix-Multiplication in OpenCL

With the development of the uniform processor designs implemented in modern graphics processing units (GPUs) released by NVIDIA and AMD, the expansive development of parallel languages and frameworks allows us to create high performance parallel applications that exploit the architectural characteristics of these devices. Specifically, we look at the modern GPU memory hierarchy, the development...