high performance computing on graphics processing units: hgpu.org

Heterogeneous computing is moving into the mainstream, and a broader range of applications are already on the way. As the provider of world-class CPUs, GPUs, and APUs, AMD offers unique insight into these technologies and how they interoperate. Attend the AMD Fusion Developer Summit to learn about the opportunities that lie ahead.

With systems such as Road Runner, there is a trend in super computing to offload parallel tasks to special purpose co-processors, composed of many relatively simple scalar processors. The cheaper commodity class equivalent of such a processor would be the graphics card, potentially offering super computer power within the confines of a desktop PC. Graphics […]

We study the use of massively parallel architectures for computing a matrix inverse. Two different algorithms are reviewed, the traditional approach based on Gaussian elimination and the Gauss-Jordan elimination alternative, and several high performance implementations are presented and evaluated. The target architecture is a current general-purpose multicore processor (CPU) connected to a graphics processor (GPU). […]

We present a high-speed dense stereo algorithm that achieves both good quality results and very high disparity estimation throughput on the graphics processing unit (GPU). The key idea is a variable center-biased windowing approach, enabling an adaptive selection of the most suitable support patterns with varying sizes and shapes. As the fundamental construct for variable […]

We propose a system-independent representation of sparse matrix formats that allows a compiler to generate efficient, system-specific code for sparse matrix operations. To show the viability of such a representation we have developed a compiler that generates and tunes code for sparse matrix-vector multiplication (SpMV) on GPUs. We evaluate our framework on six state-of-the-art matrix […]

Graphics processors are increasingly used in scientific applications due to their high computational power, which comes from hardware with multiple-level parallelism and memory hierarchy. Sparse matrix computations frequently arise in scientific applications, for example, when solving PDEs on unstructured grids. However, traditional sparse matrix algorithms are difficult to efficiently parallelize for GPUs due to irregular […]

This paper presents a study of the computational enhancement of a Graphics Processing Unit (GPU) enabled 2D flood model. The objectives are to demonstrate the significant speedup of a new GPU-enabled full dynamic wave flood model and to present the effect of model spatial resolution on its speedup. A 2D dynamic flood model based on […]

A parallel implementation via CUDA of the dynamic programming method for the knapsack problem on NVIDIA GPU is presented. A GTX 260 card with 192 cores (1.4GHz) is used for computational tests and processing times obtained with the parallel code are compared to the sequential one on a CPU with an Intel Xeon 3.0GHz. The […]

Graphics Processing Unit (GPU) has become an attractive coprocessor for scientific computing due to its massive processing capability. The sparse matrix-vector multiplication (SpMV) is a critical operation in a wide variety of scientific and engineering applications, such as sparse linear algebra and image processing. This paper presents an auto-tuning framework that can automatically compute and […]

The significant growth in computational power of modern Graphics Processing Units (GPUs) coupled with the advent of general purpose programming environments like NVIDIA’s CUDA, has seen GPUs emerging as a very popular parallel computing platform. Till recently, there has not been a performance model for GPGPUs. The absence of such a model makes it difficult […]

Particle-in-cell (PIC) simulations with Monte-Carlo collisions are used in plasma science to explore a variety of kinetic effects. One major problem is the long run-time of such simulations. Even on modern computer systems, PIC codes take a considerable amount of time for convergence. Most of the computations can be massively parallelized, since particles behave independently […]

General-Purpose Computing on Graphics Processing Units (GPGPU) has lately been of great interest due to the release of architectures and software that simplifies programming graphics cards. This study explores how performance scales with FIR digital filters by varying the number of taps and the samples. We also discuss the trade-offs with various techniques for GPGPU […]