## Posts

Oct, 27

### Solving the Boltzmann Equation on GPU

We show how to accelerate the direct solution of the Boltzmann equation using Graphics Processing Units (GPUs). In order to fully exploit the computational power of the GPU, we choose a method of solution which combines a finite difference discretization of the free-streaming term with a Monte Carlo evaluation of the collision integral. The efficiency […]

Oct, 27

### Fast calculation of HELAS amplitudes using graphics processing unit (GPU)

We use the graphics processing unit (GPU) for fast calculations of helicity amplitudes of physics processes. As our first attempt, we compute $ubaruto nγ$ ($n=2$ to 8) processes in $pp$ collisions at $sqrts = 14$TeV by transferring the MadGraph generated HELAS amplitudes (FORTRAN) into newly developed HEGET ( HELAS Evaluation with GPU Enhanced Technology) codes […]

Oct, 27

### Lattice Boltzmann based PDE solver on the GPU

In this paper, we propose a hardware-accelerated PDE (partial differential equation) solver based on the lattice Boltzmann model (LBM). The LBM is initially designed to solve fluid dynamics by constructing simplified microscopic kinetic models. As an explicit numerical scheme with only local operations, it has the advantage of being easy to implement and especially suitable […]

Oct, 27

### A Scalable graph-cut algorithm for N-D grids

Global optimisation via s-t graph cuts is widely used in computer vision and graphics. To obtain high-resolution output, graph cut methods must construct massive N-D grid-graphs containing billions of vertices. We show that when these graphs do not fit into physical memory, current max-flow/min-cut algorithms-the workhorse of graph cut methods-are totally impractical. Others have resorted […]

Oct, 27

### Implementing an Interior Point Method for Linear Programs on a CPU-GPU System

Graphics processing units (GPUs), present in every laptop and desktop computer, are potentially powerful computational engines for solving numerical problems. We present a CPU-GPU algorithm for solving linear programming problems using interior point methods. This algorithm, based on the rectangular-packed matrix storage scheme of Gunnels and Gustavson, uses the GPU for computationally intensive tasks such […]

Oct, 27

### Performance evaluation of H.264/AVC decoding and visualization using the GPU

The coding efficiency of the H.264/AVC standard makes the decoding process computationally demanding. This has limited the availability of cost-effective, high-performance solutions. Modern computers are typically equipped with powerful yet cost-effective Graphics Processing Units (GPUs) to accelerate graphics operations. These GPUs can be addressed by means of a 3-D graphics API such as Microsoft Direct3D […]

Oct, 27

### Fast k Nearest Neighbor Search using GPU

The recent improvements of graphics processing units (GPU) offer to the computer vision community a powerful processing platform. Indeed, a lot of highly-parallelizable computer vision problems can be significantly accelerated using GPU architecture. Among these algorithms, the k nearest neighbor search (KNN) is a well-known problem linked with many applications such as classification, estimation of […]

Oct, 27

### Sequence alignment with GPU: Performance and design challenges

In bioinformatics, alignments are commonly performed in genome and protein sequence analysis for gene identification and evolutionary similarities. There are several approaches for such analysis, each varying in accuracy and computational complexity. Smith-Waterman (SW) is by far the best algorithm for its accuracy in similarity scoring. However, execution time of this algorithm on general purpose […]

Oct, 27

### An improved study of real-time fluid simulation on GPU

Taking advantage of the parallelism and programmability of GPU, we solve the fluid dynamics problem completely on GPU. Different from previous methods, the whole computation is accelerated in our method by packing the scalar and vector variables into four channels of texels. In order to be adaptive to the arbitrary boundary conditions, we group the […]

Oct, 27

### GPU-Quicksort: A practical Quicksort algorithm for graphics processors

In this article, we describe GPU-Quicksort, an efficient Quicksort algorithm suitable for highly parallel multicore graphics processors. Quicksort has previously been considered an inefficient sorting solution for graphics processors, but we show that in CUDA, NVIDIA’s programing platform for general-purpose computations on graphical processors, GPU-Quicksort performs better than the fastest-known sorting implementations for graphics processors, […]

Oct, 27

### GPU accelerated pathfinding

In the past few years the graphics programmable processor (GPU) has evolved into an increasingly convincing computational resource for non graphics applications. The GPU is especially well suited to address problem sets expressed as data parallel computation with the same program executed on many data elements concurrently. In pursuing a scalable navigation planning approach for […]

Oct, 27

### Mass-spring systems on the GPU

We present and analyze different implementations of mass-spring systems for interactive simulation of deformable surfaces on graphics processing units (GPUs). For the amount of springs we target, numerical time integration of spring displacements needs to be accelerated and the transfer of displaced point positions for rendering must be avoided. To fulfill these requirements, we exploit […]