Graphics Processing Units Applications Research Articles

Dynamic voltage and frequency scaling framework for low-power embedded GPUs

Computational power of embedded graphics processing units (GPUs) in mobile system-on-chips has been increasing steadily to provide high quality user experience related to 2D and 3D graphics. Moreover, the architecture of embedded GPUs is evolving from graphic accelerators into streaming multiprocessors, which enables programmers to use GPUs for general parallel processing. Workload variations in mobile GPU applications are explored to discover the potential of dynamic voltage and frequency scaling (DVFS) on embedded GPUs. A DVFS framework which considers GPUs as computing devices, not graphic accelerators, is proposed. The experimental results show that a conventional processor DVFS policy can achieve power reduction of embedded GPUs with reasonable performance degradation.

Particle swarm optimisation (PSO)-based tool path planning for 5-axis flank milling accelerated by graphics processing unit (GPU)

Multi-axis machining offers higher machining efficiency and superior shaping capability compared to 3-axis machining. Machining error control is a critical issue in 5-axis flank milling of complex geometries and there is still a lack of solutions. Previous studies have shown that optimisation-based tool path planning is a feasible approach to reduction of machining error. However, the error estimation is time-consuming in the optimisation process, thus limiting the practicality of this approach. In this work, we apply graphics processing unit (GPU) computing technology to solve this problem. A particle swarm optimisation (PSO)-based optimisation scheme is developed to generate a series of cutter locations (CLs) that produce a minimised error on the machined surface. The error amount induced by each CL is simultaneously calculated by the parallel processing units of GPU. The PSO search process driven by the aggregated result is effectively accelerated. Test results show that our approach outperforms previous optimisation methods in both solution quality and computation efficiency. This work demonstrates a novel application of GPU on Computer Aided Manufacturing/Computer Numerical Control.

On-the-fly elimination of dynamic irregularities for GPU computing

The power-efficient massively parallel Graphics Processing Units (GPUs) have become increasingly influential for general-purpose computing over the past few years. However, their efficiency is sensitive to dynamic irregular memory references and control flows in an application. Experiments have shown great performance gains when these irregularities are removed. But it remains an open question how to achieve those gains through software approaches on modern GPUs. This paper presents a systematic exploration to tackle dynamic irregularities in both control flows and memory references. It reveals some properties of dynamic irregularities in both control flows and memory references, their interactions, and their relations with program data and threads. It describes several heuristics-based algorithms and runtime adaptation techniques for effectively removing dynamic irregularities through data reordering and job swapping. It presents a framework, G-Streamline, as a unified software solution to dynamic irregularities in GPU computing. G-Streamline has several distinctive properties. It is a pure software solution and works on the fly, requiring no hardware extensions or offline profiling. It treats both types of irregularities at the same time in a holistic fashion, maximizing the whole-program performance by resolving conflicts among optimizations. Its optimization overhead is largely transparent to GPU kernel executions, jeopardizing no basic efficiency of the GPU application. Finally, it is robust to the presence of various complexities in GPU applications. Experiments show that G-Streamline is effective in reducing dynamic irregularities in GPU computing, producing speedups between 1.07 and 2.5 for a variety of applications.

On-the-fly elimination of dynamic irregularities for GPU computing

The power-efficient massively parallel Graphics Processing Units (GPUs) have become increasingly influential for general-purpose computing over the past few years. However, their efficiency is sensitive to dynamic irregular memory references and control flows in an application. Experiments have shown great performance gains when these irregularities are removed. But it remains an open question how to achieve those gains through software approaches on modern GPUs. This paper presents a systematic exploration to tackle dynamic irregularities in both control flows and memory references. It reveals some properties of dynamic irregularities in both control flows and memory references, their interactions, and their relations with program data and threads. It describes several heuristics-based algorithms and runtime adaptation techniques for effectively removing dynamic irregularities through data reordering and job swapping. It presents a framework, G-Streamline, as a unified software solution to dynamic irregularities in GPU computing. G-Streamline has several distinctive properties. It is a pure software solution and works on the fly, requiring no hardware extensions or offline profiling. It treats both types of irregularities at the same time in a holistic fashion, maximizing the whole-program performance by resolving conflicts among optimizations. Its optimization overhead is largely transparent to GPU kernel executions, jeopardizing no basic efficiency of the GPU application. Finally, it is robust to the presence of various complexities in GPU applications. Experiments show that G-Streamline is effective in reducing dynamic irregularities in GPU computing, producing speedups between 1.07 and 2.5 for a variety of applications.

Application of graphics processing unit (GPU) to software in elementary particle/high energy physics field

Abstract We apply graphics processing unit (GPU) to software in elementary particle and high energy physics field. We develop GPU versions of programs for fast calculations of cross sections of Standard Model (SM) processes at the Large Hadron Colliders (LHC) and those for numerical integration of multi-dimensional differential cross sections of these processes via the Monte Carlo method. We summarize our activity on the development of programs which run on GPU and the results on their performances. Compared with the CPU programs, we obtain around 50 times on average better performance on GPU. We expect that application of GPU to more general programs which are used in jobs on GRID will greatly reduce the computing resources necessary for large scale reconstructions and simulations for the LHC analysis.

Polymer Dynamic Field Theory on Graphics Processing Units

This paper explores the application of graphics processing units (GPUs) to a study of the dynamics of diblock copolymer (DBCP) melts. DBCP melts exhibit ordered mesophases with potential applications in nanotechnology, but are often found out of equilibrium. The length scales involved have previously rendered the numerical modelling of these materials intractable in certain regimes. We adapt a code originally written in parallel using the Message Passing Interface to GPUs using the NVIDIA® CUDATM architecture. We gain a factor of 30 performance improvement over the original code at large system size. We then use this performance improvement to study DBCP melts in two computationally time-intensive regimes: droplet nucleation close to phase coexistence, and dynamics under confinement in a small cylindrical nanopore.

Application of GPU to computational multiphase fluid dynamics

The MARS (Multi-interfaces Advection and Reconstruction Solver) [1] is one of the surface volume tracking methods for multi-phase flows. Nowadays, the performance of GPU (Graphics Processing Unit) is much higher than the CPU (Central Processing Unit). In this study, the GPU was applied to the MARS in order to accelerate the computation of multi-phase flows (GPU-MARS), and the performance of the GPU-MARS was discussed. From the performance of the interface tracking method for the analyses of one-directional advection problem, it is found that the computing time of GPU(single GTX280) was around 4 times faster than that of the CPU (Xeon 5040, 4 threads parallelized). From the performance of Poisson Solver by using the algorithm developed in this study, it is found that the performance of the GPU showed around 30 times faster than that of the CPU. Finally, it is confirmed that the GPU showed the large acceleration of the fluid flow computation (GPU-MARS) compared to the CPU. However, it is also found that the double-precision computation of the GPU must perform with very high precision.

Application of graphics processing units to search pipelines for gravitational waves from coalescing binaries of compact objects

We report a novel application of a graphics processing unit (GPU) for the purpose of accelerating the search pipelines for gravitational waves from coalescing binaries of compact objects. A speed-up of 16-fold in total has been achieved with an NVIDIA GeForce 8800 Ultra GPU card compared with one core of a 2.5 GHz Intel Q9300 central processing unit (CPU). We show that substantial improvements are possible and discuss the reduction in CPU count required for the detection of inspiral sources afforded by the use of GPUs.

HARNESSING THE POWER OF IDLE GPUS FOR ACCELERATION OF BIOLOGICAL SEQUENCE ALIGNMENT

This paper presents a parallel system capable of accelerating biological sequence alignment on the graphics processing unit (GPU) grid. The GPU grid in this paper is a desktop grid system that utilizes idle GPUs and CPUs in the office and home. Our parallel implementation employs a master-worker paradigm to accelerate an OpenGL-based algorithm that runs on a single GPU. We integrate this implementation into a screensaver-based grid system that detects idle resources on which the alignment code can run. We also show some experimental results comparing our implementation with three different implementations running on a single GPU, a single CPU, or multiple CPUs. As a result, we find that a single non-dedicated GPU can provide us almost the same throughput as two dedicated CPUs in our laboratory environment, where GPU-equipped machines are ordinarily used to develop GPU applications. In a dedicated environment, the GPU-accelerated code achieves five times higher throughput than the CPU-based code. Furthermore, a linear speedup of 30.7X is observed on a 32-node cluster of dedicated GPUs. We also implement a compute unified device architecture (CUDA) based algorithm to demonstrate further acceleration.

Air pollution modelling using a Graphics Processing Unit with CUDA

The Graphics Processing Unit (GPU) is a powerful tool for parallel computing. In the past years the performance and capabilities of GPUs have increased, and the Compute Unified Device Architecture (CUDA) – a parallel computing architecture – has been developed by NVIDIA to utilize this performance in general purpose computations. Here we show for the first time a possible application of GPU for environmental studies serving as a basement for decision making strategies. A stochastic Lagrangian particle model has been developed on CUDA to estimate the transport and the transformation of the radionuclides from a single point source during an accidental release. Our results show that parallel implementation achieves typical acceleration values in the order of 80–120 times compared to CPU using a single-threaded implementation on a 2.33 GHz desktop computer. Only very small differences have been found between the results obtained from GPU and CPU simulations, which are comparable with the effect of stochastic transport phenomena in atmosphere. The relatively high speedup with no additional costs to maintain this parallel architecture could result in a wide usage of GPU for diversified environmental applications in the near future.

GPU‐accelerated boundary element method for Helmholtz' equation in three dimensions

AbstractRecently, the application of graphics processing units (GPUs) to scientific computations is attracting a great deal of attention, because GPUs are getting faster and more programmable. In particular, NVIDIA's GPUs called compute unified device architecture enable highly mutlithreaded parallel computing for non‐graphic applications. This paper proposes a novel way to accelerate the boundary element method (BEM) for three‐dimensional Helmholtz' equation using CUDA. Adopting the techniques for the data caching and the double–single precision floating‐point arithmetic, we implemented a GPU‐accelerated BEM program for GeForce 8‐series GPUs. The program performed 6–23 times faster than a normal BEM program, which was optimized for an Intel's quad‐core CPU, for a series of boundary value problems with 8000–128000 unknowns, and it sustained a performance of 167 Gflop/s for the largest problem (1 058 000 unknowns). The accuracy of our BEM program was almost the same as that of the regular BEM program using the double precision floating‐point arithmetic. In addition, our BEM was applicable to solve realistic problems. In conclusion, the present GPU‐accelerated BEM works rapidly and precisely for solving large‐scale boundary value problems for Helmholtz' equation. Copyright © 2009 John Wiley & Sons, Ltd.

Foreword by the Editor [EM Programmer's Notebook

Recent years have seen an upsurge of interest in computational science and engineering applications in exploiting specialized computer architectures, especially graphical processing units (GPUs). Computational electro magnetics (CEM) has been no exception. The application of GPUs to CEM has been described in a number of contributions to this column, including the December 2005, June and December 2010, and the October 2012 issues. Although the technology has been in use for some years now, GPUs are not simple to program when high computational throughout is sought - and this is, after all, the main motivation for using such devices. In this month's contribution, the authors discuss some techniques for simplifying this, working in the context of the CUDA development environment. We thank the authors for their useful contribution on a topic of very considerable current interest.