## Posts

Oct, 27

### Efficient GPU-Based Texture Interpolation using Uniform B-Splines

This article presents uniform B-spline interpolation, completely contained on the graphics processing unit (GPU). This implies that the CPU does not need to compute any lookup tables or B-spline basis functions. The cubic interpolation can be decomposed into several linear interpolations [Sigg and Hadwiger 05], which are hard-wired on the GPU and therefore very fast. […]

Oct, 27

### GPU-based ultrafast IMRT plan optimization

The widespread adoption of on-board volumetric imaging in cancer radiotherapy has stimulated research efforts to develop online adaptive radiotherapy techniques to handle the inter-fraction variation of the patient’s geometry. Such efforts face major technical challenges to perform treatment planning in real time. To overcome this challenge, we are developing a supercomputing online re-planning environment (SCORE) […]

Oct, 27

### Understanding the efficiency of GPU algorithms for matrix-matrix multiplication

Utilizing graphics hardware for general purpose numerical computations has become a topic of considerable interest. The implementation of streaming algorithms, typified by highly parallel computations with little reuse of input data, has been widely explored on GPUs. We relax the streaming model’s constraint on input reuse and perform an in-depth analysis of dense matrix-matrix multiplication, […]

Oct, 27

### GPU Based Acceleration of Telegraph Equation

In a matter of just a few years, the programmable graphics processor unit has evolved into an absolute computing workhorse. With multiple cores driven by very high memory bandwidth, today’s GPUs offer incredible resources for both graphics and non-graphics processing. An original mathematical method “Modern Taylor Series Method” (MTSM) which uses the Taylor series method […]

Oct, 27

### Importance of Explicit Vectorization for CPU and GPU Software Performance

Much of the current focus in high-performance computing is on multi-threading, multi-computing, and graphics processing unit (GPU) computing. However, vectorization and non-parallel optimization techniques, which can often be employed additionally, are less frequently discussed. In this paper, we present an analysis of several optimizations done on both central processing unit (CPU) and GPU implementations of […]

Oct, 27

### GPU computing for systems biology

The development of detailed, coherent, models of complex biological systems is recognized as a key requirement for integrating the increasing amount of experimental data. In addition, in-silico simulation of bio-chemical models provides an easy way to test different experimental conditions, helping in the discovery of the dynamics that regulate biological systems. However, the computational power […]

Oct, 27

### On the limits of GPU acceleration

This paper throws a small “wet blanket” on the hot topic of GPGPU acceleration, based on experience analyzing and tuning both multithreaded CPU and GPU implementations of three computations in scientific computing. These computations–(a) iterative sparse linear solvers; (b) sparse Cholesky factorization; and (c) the fast multipole method–exhibit complex behavior and vary in computational intensity […]

Oct, 27

### GPU sample sort

In this paper, we present the design of a sample sort algorithm for manycore GPUs. Despite being one of the most efficient comparison-based sorting algorithms for distributed memory architectures its performance on GPUs was previously unknown. For uniformly distributed keys our sample sort is at least 25% and on average 68% faster than the best […]

Oct, 27

### Accelerating video decoding using GPU

Most modern computers or game consoles are equipped with powerful graphics processing units (GPU) to accelerate graphics operations. There is a trend that the power of GPU outgrows that of the CPU (central processing unit). However, the GPU engines are specially designed for graphics operations. Can we take advantage of the powerful GPU engines for […]

Oct, 27

### GPU implementation of neural networks

Graphics processing unit (GPU) is used for a faster artificial neural network. It is used to implement the matrix multiplication of a neural network to enhance the time performance of a text detection system. Preliminary results produced a 20-fold performance enhancement using an ATI RADEON 9700 PRO board. The parallelism of a GPU is fully […]

Oct, 27

### The GPU Computing Era

GPU computing is at a tipping point, becoming more widely used in demanding consumer applications and high-performance computing. This article describes the rapid evolution of GPU architectures-from graphics processors to massively parallel many-core multiprocessors, recent developments in GPU computing architectures, and how the enthusiastic adoption of CPU+GPU coprocessing is accelerating parallel applications.

Oct, 27

### GPU Computing

The graphics processing unit (GPU) has become an integral part of today’s mainstream computing systems. Over the past six years, there has been a marked increase in the performance and capabilities of GPUs. The modern GPU is not only a powerful graphics engine but also a highly parallel programmable processor featuring peak arithmetic and memory […]