USING GPU NVIDIA FOR LINEAR ALGEBRA PROLEMS

Accelerating GPU Kernels for Dense Linear Algebra

Implementations of the Basic Linear Algebra Subprograms (BLAS) interface are major building block of dense linear algebra (DLA) libraries, and therefore have to be highly optimized. We present some techniques and implementations that significantly accelerate the corresponding routines from currently available libraries for GPUs. In particular, Pointer Redirecting – a set of GPU specific optimiz...

Sparse linear algebra on a GPU

We investigate what the graphics processing units (GPUs) have to offer compared to the central processing units (CPUs) when solving a sparse linear system of equations. This is performed by using a GPU to simulate fluid-flow in a porous medium. Flow-problems are discretized mainly by the mimetic finite element discretization, but also by a two-point fluxapproximation (TPFA) method. Both of thes...

Performing DCT8x8 Computation on GPU Using NVIDIA CUDA Technology

In this paper, we have proposed sequential and parallel Discrete Cosine Transform (DCT) in compute unified device architecture (CUDA) libraries. The introduction of programmable pipeline in the graphics processing units (GPU) has enabled configurability. GPU which is available in every computer has a tremendous feat of highly parallel SIMD processing, but its capability is often under-utilized....

GPU acceleration of object classification algorithms using NVIDIA CUDA

Data access optimized applications on the GPU using NVIDIA CUDA

This work is an attempt to address the problem of bandwidth limited performance of data intensive GPGPU applications. Performance limited by memory bandwidth is common issue faced by general data intensive HPC applications. In case of the GPU, this problem is more pronounced owing to the unique architecture. This problem has been tackled by optimizing basic data rearrangement operations on the ...