Survey Report on Multi GPU Implementation in Open Source CFD Solver

A CFD (Computational Fluid Dynamics) solver is used for simulation of fluid flow and heat transfer. It helps in analyzing the various parameters in fluid flow of scientific applications. OpenFOAM (Open Field Operation and Manipulation) [14] is an open source Software which can be used to solve Computational Fluid Dynamics (CFD) problems. The OpenFOAM solves various equations e.g. Mass Conservation, Conjugate Heat Transformer. OpenFOAM solves CFD problems serially as well as in parallel. The motivation of the project is to reduce time for solving CFD problems by implementing it on GPU. The OpenFOAM a software contribution which (Open Field Operation and Manipulation) is open source Tool. OpenFOAM has a many of features to get answer to anything from complex liquid (or gas) moves getting mixed in trouble chemical reactions, turbulence and heat give property in law, to solid driving power and electromagnetic. Existing OpenFOAM executes serially, by introducing parallelism, time required for execution of OpenFOAM can be reduced. For introducing parallelism, System requires CUDA (Compute Unified Device Architecture) enabled CPU (Central Processing Unit). To achieve parallelism, openFOAM requires CUDA support and support is achieved by of gpu library. Library is used to introduce parallelism for single GPU and multigpu can improve performance of OpenFOAM

GPU Accelerated Prognostics

Prognostic methods enable operators and maintainers to predict the future performance for critical systems. However, these methods can be computationally expensive and may need to be performed each time new information about the system becomes available. In light of these computational requirements, we have investigated the application of graphics processing units (GPUs) as a computational platform for real-time prognostics. Recent advances in GPU technology have reduced cost and increased the computational capability of these highly parallel processing units, making them more attractive for the deployment of prognostic software. We present a survey of model-based prognostic algorithms with considerations for leveraging the parallel architecture of the GPU and a case study of GPU-accelerated battery prognostics with computational performance results

Accelerating Climate and Weather Simulations through Hybrid Computing

Unconventional multi- and many-core processors (e.g. IBM (R) Cell B.E.(TM) and NVIDIA (R) GPU) have emerged as effective accelerators in trial climate and weather simulations. Yet these climate and weather models typically run on parallel computers with conventional processors (e.g. Intel, AMD, and IBM) using Message Passing Interface. To address challenges involved in efficiently and easily connecting accelerators to parallel computers, we investigated using IBM's Dynamic Application Virtualization (TM) (IBM DAV) software in a prototype hybrid computing system with representative climate and weather model components. The hybrid system comprises two Intel blades and two IBM QS22 Cell B.E. blades, connected with both InfiniBand(R) (IB) and 1-Gigabit Ethernet. The system significantly accelerates a solar radiation model component by offloading compute-intensive calculations to the Cell blades. Systematic tests show that IBM DAV can seamlessly offload compute-intensive calculations from Intel blades to Cell B.E. blades in a scalable, load-balanced manner. However, noticeable communication overhead was observed, mainly due to IP over the IB protocol. Full utilization of IB Sockets Direct Protocol and the lower latency production version of IBM DAV will reduce this overhead

CUDA Implementation of a Navier-Stokes Solver on Multi-GPU Desktop Platforms for Incompressible Flows

Graphics processor units (GPU) that are traditionally designed for graphics rendering have emerged as massively-parallel co-processors to the central processing unit (CPU). Small-footprint desktop supercomputers with hundreds of cores that can deliver teraflops peak performance at the price of conventional workstations have been realized. A computational fluid dynamics (CFD) simulation capability with rapid computational turnaround time has the potential to transform engineering analysis and design optimization procedures. We describe the implementation of a Navier-Stokes solver for incompressible fluid flow using desktop platforms equipped with multi-GPUs. Specifically, NVIDIA’s Compute Unified Device Architecture (CUDA) programming model is used to implement the discretized form of the governing equations. The projection algorithm to solve the incompressible fluid flow equations is divided into distinct CUDA kernels, and a unique implementation that exploits the memory hierarchy of the CUDA programming model is suggested. Using a quad-GPU platform, we observe two orders of magnitude speedup relative to a serial CPU implementation. Our results demonstrate that multi-GPU desktops can serve as a cost-effective small-footprint parallel computing platform to accelerate CFD simulations substantially. I. Introductio

Seguimiento 4D de microorganismos con microscopía holográfica digital sin lentes a velocidad de video por medio de GPGPU

Resumen: La presente tesis de Maestría en Ingeniería de Sistemas se centra en el seguimiento 4D de microorganismos a velocidad de video. Por medio de la microscopía holográfica digital sin lentes se adquiere la información del sistema microscópico y con técnicas de reconocimiento de patrones en el procesamiento digital de imágenes se lleva a cabo su seguimiento 4D (espacio 3D y tiempo). Ambas técnicas son implementadas con soporte en la arquitectura GPGPU para su operación a velocidad de video. La validez científica y tecnológica de la tesis subyace en el hecho que el seguimiento de microorganismos a ratas de video potencializará distintas aplicaciones en áreas como la salud, el monitoreo del medio ambiente y el combate del terrorismo biológico, entre otros. La metodología desarrollada en esta tesis permite seguir en 4D microorganismos de dimensiones en el orden de los 100 μm en volúmenes de muestra de 1mm3, a una velocidad máxima de 8 cuadros por segundo.Abstract: This Master’s thesis in Systems Engineering focuses on the monitoring of microorganisms in 4D at video rate. By using digital lensless holographic microscopy the microscopic information of the system is acquired and with pattern recognition techniques in digital image processing it is carried out the tracking in 4D (3D in space and time). Both techniques are implemented in the GPGPU architecture for operation at video rate. The technological and scientific validity of the thesis lies on the fact that the monitoring of microorganisms at video rate will potentiate different applications in areas such as health, environmental monitoring and combating bioterrorism, among others. The methodology developed in this thesis can track in 4D microorganisms of dimensions in the order of 100 μm, in 1 mm3 sample volumes, at a maximum rate of 8 frames per second.Maestrí