Practical Examples of Automated Development of Efficient Parallel Programs

2021 ◽  
pp. 180-216
Keyword(s):  
Graphics Processing Units ◽  
Dynamic Models ◽  
Multicore Processors ◽  
Central Processing ◽  
Design And Synthesis ◽  
Cuda Technology ◽  
Design Generation ◽  
Graphics Processing ◽  
Rewriting Rules ◽  
Target Platform

In this chapter, some examples of application of the developed software tools for design, generation, transformation, and optimization of programs for multicore processors and graphics processing units are considered. In particular, the algebra-algorithmic-integrated toolkit for design and synthesis of programs (IDS) and the rewriting rules system TermWare.NET are applied for design and parallelization of programs for multicore central processing units. The developed algebra-dynamic models and the rewriting rules toolkit are used for parallelization and optimization of programs for NVIDIA GPUs supporting the CUDA technology. The TuningGenie framework is applied for parallel program auto-tuning: optimization of sorting, Brownian motion simulation, and meteorological forecasting programs to a target platform. The parallelization of Fortran programs using the rewriting rules technique on sample problems in the field of quantum chemistry is examined.

2012 Freeman Scholar Lecture: Computational Fluid Dynamics on Graphics Processing Units

2013 ◽  
Vol 135 (6) ◽  
Author(s):  
S. P. Vanka
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Graphics Processing Units ◽  
Stokes Equations ◽  
Multicore Processors ◽  
Fluid Flows ◽  
Data Access ◽  
Central Processing ◽  
Data Parallel ◽  
Graphics Processing

This paper discusses the various issues of using graphics processing units (GPU) for computing fluid flows. GPUs, used primarily for processing graphics functions in a computer, are massively parallel multicore processors, which can also perform scientific computations in a data parallel mode. In the past ten years, GPUs have become quite powerful and have challenged the central processing units (CPUs) in their price and performance characteristics. However, in order to fully benefit from the GPUs' performance, the numerical algorithms must be made data parallel and converge rapidly. In addition, the hardware features of the GPUs require that the memory access be managed carefully in order to not suffer from the high latency. Fully explicit algorithms for Euler and Navier–Stokes equations and the lattice Boltzmann method for mesoscopic flows have been widely incorporated on the GPUs, with significant speed-up over a scalar algorithm. However, more complex algorithms with implicit formulations and unstructured grids require innovative thinking in data access and management. This article reviews the literature on linear solvers and computational fluid dynamics (CFD) algorithms on GPUs, including the author's own research on simulations of fluid flows using GPUs.

scholarly journals TESLA GPUs versus MPI with OpenMP for the Forward Modeling of Gravity and Gravity Gradient of Large Prisms Ensemble

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Carlos Couder-Castañeda ◽  
Carlos Ortiz-Alemán ◽  
Mauricio Gabriel Orozco-del-Castillo ◽  
Mauricio Nava-Flores
Keyword(s):  
Parallel Computing ◽  
Graphics Processing Units ◽  
Forward Modeling ◽  
Gravity Gradient ◽  
Constant Density ◽  
Gravitational Fields ◽  
Design And Implementation ◽  
Cuda Technology ◽  
Performance Results ◽  
Graphics Processing

An implementation with the CUDA technology in a single and in several graphics processing units (GPUs) is presented for the calculation of the forward modeling of gravitational fields from a tridimensional volumetric ensemble composed by unitary prisms of constant density. We compared the performance results obtained with the GPUs against a previous version coded in OpenMP with MPI, and we analyzed the results on both platforms. Today, the use of GPUs represents a breakthrough in parallel computing, which has led to the development of several applications with various applications. Nevertheless, in some applications the decomposition of the tasks is not trivial, as can be appreciated in this paper. Unlike a trivial decomposition of the domain, we proposed to decompose the problem by sets of prisms and use different memory spaces per processing CUDA core, avoiding the performance decay as a result of the constant calls to kernels functions which would be needed in a parallelization by observations points. The design and implementation created are the main contributions of this work, because the parallelization scheme implemented is not trivial. The performance results obtained are comparable to those of a small processing cluster.

Heterogenous Computing on Iris Matching with OpenCL

2016 ◽  
Vol 850 ◽  
pp. 129-135
Author(s):  
Buğra Şimşek ◽  
Nursel Akçam
Keyword(s):  
Graphics Processing Units ◽  
Iris Recognition ◽  
Heterogeneous Computing ◽  
Hamming Distance ◽  
Heterogeneous Systems ◽  
Digital Signal ◽  
Mobile Platforms ◽  
Central Processing ◽  
Field Programmable ◽  
Graphics Processing

This study presents parallelization of Hamming Distance algorithm, which is used for iris comparison on iris recognition systems, for heterogeneous systems that can be included Central Processing Units (CPUs), Graphics Processing Units (GPUs), Digital Signal Processing (DSP) boards, Field Programmable Gate Array (FPGA) and some other mobile platforms with OpenCL. OpenCL allows to run same code on CPUs, GPUs, FPGAs and DSP boards. Heterogeneous computing refers to systems include different kind of devices (CPUs, GPUs, FPGAs and other accelerators). Heterogeneous computing gains performance or reduces power for suitable algorithms on these OpenCL supported devices. In this study, Hamming Distance algorithm has been coded with C++ as a sequential code and has been parallelized a designated method by us with OpenCL. Our OpenCL code has been executed on Nvidia GT430 GPU and Intel Xeon 5650 processor. The OpenCL code implementation demonstrates that speed up to 87 times with parallelization. Also our study differs from other studies, which accelerate iris matching, with regard to ensure heterogeneous computing by using OpenCL.

scholarly journals The VOLNA-OP2 tsunami code (version 1.5)

2018 ◽  
Vol 11 (11) ◽  
pp. 4621-4635 ◽  
Author(s):  
Istvan Z. Reguly ◽  
Daniel Giles ◽  
Devaraj Gopinathan ◽  
Laure Quivy ◽  
Joakim H. Beck ◽  
...  
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
Shallow Water Equation ◽  
Xeon Phi ◽  
Intel Xeon Phi ◽  
Central Processing ◽  
Domain Specific ◽  
Computing Platforms ◽  
Graphics Processing ◽  
Intel Xeon

Abstract. In this paper, we present the VOLNA-OP2 tsunami model and implementation; a finite-volume non-linear shallow-water equation (NSWE) solver built on the OP2 domain-specific language (DSL) for unstructured mesh computations. VOLNA-OP2 is unique among tsunami solvers in its support for several high-performance computing platforms: central processing units (CPUs), the Intel Xeon Phi, and graphics processing units (GPUs). This is achieved in a way that the scientific code is kept separate from various parallel implementations, enabling easy maintainability. It has already been used in production for several years; here we discuss how it can be integrated into various workflows, such as a statistical emulator. The scalability of the code is demonstrated on three supercomputers, built with classical Xeon CPUs, the Intel Xeon Phi, and NVIDIA P100 GPUs. VOLNA-OP2 shows an ability to deliver productivity as well as performance and portability to its users across a number of platforms.

TOWARDS FFT-BASED DIRECT NUMERICAL SIMULATIONS OF TURBULENT FLOWS ON A GPU

2013 ◽  
Vol 05 (01) ◽  
pp. 1350014 ◽  
Author(s):  
CATHERINE RUCKI ◽  
ABHILASH J. CHANDY
Keyword(s):  
Turbulent Flows ◽  
Graphics Processing Units ◽  
Turbulence Models ◽  
Science And Engineering ◽  
Computing Power ◽  
Central Processing ◽  
General Improvement ◽  
Highly Turbulent Flows ◽  
Graphics Processing ◽  
Pseudo Spectral

The accurate simulation of turbulence and the implementation of corresponding turbulence models are both critical to the understanding of the complex physics behind turbulent flows in a variety of science and engineering applications. Despite the tremendous increase in the computing power of central processing units (CPUs), direct numerical simulation of highly turbulent flows is still not feasible due to the need for resolving the smallest length scale, and today's CPUs cannot keep pace with demand. The recent development of graphics processing units (GPU) has led to the general improvement in the performance of various algorithms. This study investigates the applicability of GPU technology in the context of fast-Fourier transform (FFT)-based pseudo-spectral methods for DNS of turbulent flows for the Taylor–Green vortex problem. They are implemented on a single GPU and a speedup of unto 31x is obtained in comparison to a single CPU.

scholarly journals PI-FLAME: A parallel immune system simulator using the FLAME graphic processing unit environment

SIMULATION ◽  
2016 ◽  
Vol 93 (1) ◽  
pp. 69-84 ◽  
Author(s):  
Shailesh Tamrakar ◽  
Paul Richmond ◽  
Roshan M D’Souza
Keyword(s):  
Immune System ◽  
Graphics Processing Units ◽  
Processing Unit ◽  
Human Immune System ◽  
Innate And Adaptive Immunity ◽  
Agent Based ◽  
Central Processing ◽  
Agent Simulation ◽  
Study Population ◽  
Graphics Processing

Agent-based models (ABMs) are increasingly being used to study population dynamics in complex systems, such as the human immune system. Previously, Folcik et al. (The basic immune simulator: an agent-based model to study the interactions between innate and adaptive immunity. Theor Biol Med Model 2007; 4: 39) developed a Basic Immune Simulator (BIS) and implemented it using the Recursive Porous Agent Simulation Toolkit (RePast) ABM simulation framework. However, frameworks such as RePast are designed to execute serially on central processing units and therefore cannot efficiently handle large model sizes. In this paper, we report on our implementation of the BIS using FLAME GPU, a parallel computing ABM simulator designed to execute on graphics processing units. To benchmark our implementation, we simulate the response of the immune system to a viral infection of generic tissue cells. We compared our results with those obtained from the original RePast implementation for statistical accuracy. We observe that our implementation has a 13× performance advantage over the original RePast implementation.

An Accelerated 3D Navier–Stokes Solver for Flows in Turbomachines

2010 ◽  
Vol 133 (2) ◽  
Author(s):  
Tobias Brandvik ◽  
Graham Pullan
Keyword(s):  
Graphics Processing Units ◽  
Three Dimensional ◽  
Navier Stokes ◽  
Linear Scaling ◽  
Test Case ◽  
Processing Unit ◽  
Central Processing ◽  
Order Of Magnitude ◽  
Graphics Processing ◽  
Good Agreement

A new three-dimensional Navier–Stokes solver for flows in turbomachines has been developed. The new solver is based on the latest version of the Denton codes but has been implemented to run on graphics processing units (GPUs) instead of the traditional central processing unit. The change in processor enables an order-of-magnitude reduction in run-time due to the higher performance of the GPU. The scaling results for a 16 node GPU cluster are also presented, showing almost linear scaling for typical turbomachinery cases. For validation purposes, a test case consisting of a three-stage turbine with complete hub and casing leakage paths is described. Good agreement is obtained with previously published experimental results. The simulation runs in less than 10 min on a cluster with four GPUs.

scholarly journals Controllers: An abstraction to ease the use of hardware accelerators

2017 ◽  
Vol 32 (6) ◽  
pp. 838-853 ◽  
Author(s):  
Ana Moreton–Fernandez ◽  
Hector Ortega–Arranz ◽  
Arturo Gonzalez–Escribano
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
Abstract Entity ◽  
Hardware Accelerators ◽  
Processing Unit ◽  
Central Processing ◽  
Computing Platforms ◽  
Graphics Processing ◽  
Performance Computing ◽  
Selection Of

Nowadays the use of hardware accelerators, such as the graphics processing units or XeonPhi coprocessors, is key in solving computationally costly problems that require high performance computing. However, programming solutions for an efficient deployment for these kind of devices is a very complex task that relies on the manual management of memory transfers and configuration parameters. The programmer has to carry out a deep study of the particular data that needs to be computed at each moment, across different computing platforms, also considering architectural details. We introduce the controller concept as an abstract entity that allows the programmer to easily manage the communications and kernel launching details on hardware accelerators in a transparent way. This model also provides the possibility of defining and launching central processing unit kernels in multi-core processors with the same abstraction and methodology used for the accelerators. It internally combines different native programming models and technologies to exploit the potential of each kind of device. Additionally, the model also allows the programmer to simplify the proper selection of values for several configuration parameters that can be selected when a kernel is launched. This is done through a qualitative characterization process of the kernel code to be executed. Finally, we present the implementation of the controller model in a prototype library, together with its application in several case studies. Its use has led to reductions in the development and porting costs, with significantly low overheads in the execution times when compared to manually programmed and optimized solutions which directly use CUDA and OpenMP.

scholarly journals Graphics processing unit implementation of the F-statistic for continuous gravitational wave searches

2021 ◽  
Author(s):  
Liam Dunn ◽  
Patrick Clearwater ◽  
Andrew Melatos ◽  
Karl Wette
Keyword(s):  
Gravitational Wave ◽  
Graphics Processing Units ◽  
Graphics Processing Unit ◽  
Computational Cost ◽  
Processing Unit ◽  
Central Processing ◽  
Long Baseline ◽  
Using Data ◽  
Graphics Processing ◽  
Gpu Implementation

Abstract The F-statistic is a detection statistic used widely in searches for continuous gravitational waves with terrestrial, long-baseline interferometers. A new implementation of the F-statistic is presented which accelerates the existing "resampling" algorithm using graphics processing units (GPUs). The new implementation runs between 10 and 100 times faster than the existing implementation on central processing units without sacrificing numerical accuracy. The utility of the GPU implementation is demonstrated on a pilot narrowband search for four newly discovered millisecond pulsars in the globular cluster Omega Centauri using data from the second Laser Interferometer Gravitational-Wave Observatory observing run. The computational cost is 17:2 GPU-hours using the new implementation, compared to 1092 core-hours with the existing implementation.

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