## Posts

Nov, 2

### GPU powered CNN simulator (SIMCNN) with graphical flow based programmability

In this paper, we introduce an innovative CNN algorithm development environment that significantly assists algorithmic design. The introduced graphical user interface uses Matlab Simulink with UMF-like program description, where direct functionality accompanies better accessability. The new generation of graphical cards incorporate many general purpose graphics processing units, giving the power of parallel computing to a […]

Nov, 2

### 3D finite difference computation on GPUs using CUDA

In this paper we describe a GPU parallelization of the 3D finite difference computation using CUDA. Data access redundancy is used as the metric to determine the optimal implementation for both the stencil-only computation, as well as the discretization of the wave equation, which is currently of great interest in seismic computing. For the larger […]

Nov, 2

### Parallel external sorting for CUDA-enabled GPUs with load balancing and low transfer overhead

Sorting is a well-investigated topic in Computer Science in general and by now many efficient sorting algorithms for CPUs and GPUs have been developed. There is no swapping, paging, etc. available on GPUs to provide more virtual memory than physically available, thus if one wants to sort sequences that exceed GPU memory using the GPU […]

Nov, 2

### Linear algebra operators for GPU implementation of numerical algorithms

In this work, the emphasis is on the development of strategies to realize techniques of numerical computing on the graphics chip. In particular, the focus is on the acceleration of techniques for solving sets of algebraic equations as they occur in numerical simulation. We introduce a framework for the implementation of linear algebra operators on […]

Nov, 2

### Improving Performance of Matrix Multiplication and FFT on GPU

In this paper we discuss about our experiences in improving the performance of two key algorithms: the single-precision matrix-matrix multiplication subprogram (SGEMM of BLAS) and single-precision FFT using CUDA. The former is computation-intensive, while the latter is memory bandwidth or communication-intensive. A peak performance of 393 Gflops is achieved on NVIDIA GeForce GTX280 for the […]

Nov, 2

### Acceleration of finite-difference time-domain (FDTD) using graphics processor units (GPU)

The Finite-Difference Time-Domain (FDTD) method is used extensively in areas of microwave engineering and optics. However, FDTD runs too slow for some simulations to be practical, especially when run on standard desktop computers. The suitability of dedicated hardware for the acceleration of FDTD computations has been investigated. It is demonstrated that standard consumer Graphics Processor […]

Nov, 1

### A control-structure splitting optimization for GPGPU

Control statements in a GPU program such as loops and branches pose serious challenges for the efficient usage of GPU resources because those control statements will lead to the serialization of threads and consequently ruin the occupancy of GPU, that is, the number of threads running concurrently. Unlike traditional vector processing units that are inside […]

Nov, 1

### GPU-assisted decoding of video samples represented in the YCoCg-R color space

Although pixel shaders were designed for the creation of programmable rendering effects, they can also be used as generic processing units for vector data. In this paper, attention is paid to an implementation of the YCoCg-R to RGB color space transform, as defined in the H.264/AVC Fidelity Range Extensions, by making use of pixel shaders. […]

Nov, 1

### GPGPU: general purpose computation on graphics hardware

The graphics processor (GPU) on today’s commodity video cards has evolved into an extremely powerful and flexible processor. The latest graphics architectures provide tremendous memory bandwidth and computational horsepower, with fully programmable vertex and pixel processing units that support vector operations up to full IEEE floating point precision. High level languages have emerged for graphics […]

Nov, 1

### A GPU accelerated storage system

Massively multicore processors, like, for example, Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign […]

Nov, 1

### OpenVIDIA: parallel GPU computer vision

Graphics and vision are approximate inverses of each other: ordinarily Graphics Processing Units (GPUs) are used to convert "numbers into pictures" (i.e. computer graphics). In this paper, we propose using GPUs in approximately the reverse way: to assist in "converting pictures into numbers" (i.e. computer vision). The OpenVIDIA project uses single or multiple graphics cards […]