high performance computing on graphics processing units: hgpu.org

Parallel codes are written primarily for the purpose of performance. It is highly desirable that parallel codes be portable between parallel architectures without significant performance degradation or code rewrites. While performance portability and its limits have been studied thoroughly on single processor systems, this goal has been less extensively studied and is more difficult to […]

We present a unique and elegant graphics hardware realization of multi agent simulation. Specifically, we adapted Velocity Obstacles that suits well parallel computation on single instruction, multiple thread, SIMT, type architecture. We explore hash based nearest neighbors search to considerably optimize the algorithm when mapped on to the GPU. Moreover, to alleviate inefficiencies of agent […]

Driven by the insatiable demand of real-time graphics, especially from the market of computer games, Graphics Processing Unit (GPU) is becoming a major computing horsepower during recent years since the performance of GPU is surpassing that of the contemporary CPU. This paper presents our study on how to efficiently recover the passwords for encrypted RAR […]

Protecting data by passwords in documents such as DOC, PDF or RAR, ZIP archives has been demonstrated to be weak under dictionary attacks. Time for recovering the passwords of such documents mainly depends on two factors: the size of the password search space and the computing power of the underline system. In this paper, we […]

Graphics processing unit GPU supports data parallel computation through single instruction multi-data, and provides powerful logic computation ability. We have testified that RAR password decryption rate is greatly improved utilizing parallel computation ability of GPU.

Dealing with high-resolution imagery with billions or trillions of samples is an enormous challenge that oftenoverwhelms the graphics subsystem of any computer. Silicon Graphics, Inc. addressed this issue by providing explicit hardwaresupport for offset registers and texture sub-loads in their InfiniteReality machine. The clipmap algorithm uses sub-textures andincremental updates based on a toroidal mapping to […]

We present benchmark results of optimized dense matrix multiplication kernels for Cypress GPU. We write general matrix multiply (GEMM) kernels for single (SP), double (DP) and double-double (DDP) precision. Our SGEMM and DGEMM kernels show ~2 Tflop/s and ~470 Gflop/s, respectively. These results for SP and DP correspond to 73% and 87% of the theoretical […]

Original bump mapping is only defined for surfaces with a known surface parametrization. In this paper a new method, for the GPU, is proposed which does not use such a given parametrization. To compute the perturbed normal the only inputs used are the surface position, the height value and the original normal. The method decouples […]

In this chapter we discuss the programming environment and model for programming the NVIDIA GeForce 280 GTX GPU, NVIDIA Quadro 5800 FX, and NVIDIA GeForce 8800 GTS devices, which are the GPUs used in our implementations. We discuss the hardware model, memory model, and the programmingmodel for these devices, in order to provide background for […]

A personal computer’s graphics processing unit, or GPU, has been the seed of a growing interest in the academic and research communities of recent months. This paper investigates current technology that enables a GPU to process and solve linear algebra computations, in particular, matrix operations. Matrix operations of linear algebra are the basis of scientific […]

Compact representation of geometry using a suitable procedural or mathematical model and a ray-tracing mode of rendering fit the programmable graphics processor units (GPUs) well. Several such representations including parametric and subdivision surfaces have been explored in recent research. The important and widely applicable category of the general implicit surface has received less attention. In […]

Sparse matrix-vector multiplication (SpMV) is a common operation in numerical linear algebra and is the computational kernel of many scientific applications. It is one of the original and perhaps most studied targets for FPGA acceleration. Despite this, GPUs, which have only recently gained both general-purpose programmability and native support for double precision floating-point arithmetic, are […]