Parallel Population Based Incremental Learning With GPU Acceleration for Nonlinear Optimization

2009 ◽  
Author(s):  
Weihang Zhu
Keyword(s):  
Incremental Learning ◽  
Graphics Processing Units ◽  
Gpu Computing ◽  
Optimization Problems ◽  
Population Based ◽  
Pattern Search ◽  
Processing Unit ◽  
Central Processing ◽  
Computing Platform ◽  
Graphics Processing

This paper presents a GPU-based parallel Population Based Incremental Learning (PBIL) algorithm with a local search on bound constrained optimization problems. The genotype of an entire population is evolved in PBIL, which was derived from Genetic Algorithms. Graphics Processing Units (GPU) is an emerging technology for desktop parallel computing. In this research, the classical PBIL is adapted in the data-parallel GPU computing platform. The global optimal search of the PBIL is enhanced by a local Pattern Search method. The hybrid PBIL method is implemented in the GPU environment, and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that GPU-accelerated PBIL method is effective and faster than the corresponding CPU implementation.

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.

Advanced Topics GPU Programming and CUDA Architecture

2016 ◽  
pp. 175-203
Author(s):  
Mainak Adhikari ◽  
Sukhendu Kar
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
Programming Model ◽  
Graphics Processing Unit ◽  
Direct Access ◽  
Gpu Programming ◽  
Processing Unit ◽  
Computing Platform ◽  
Cuda Architecture ◽  
Graphics Processing

Graphics processing unit (GPU), which typically handles computation only for computer graphics. Any GPU providing a functionally complete set of operations performed on arbitrary bits can compute any computable value. Additionally, the use of multiple graphics cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing. CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs). CUDA gives program developers direct access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. This chapter first discuss some features and challenges of GPU programming and the effort to address some of the challenges with building and running GPU programming in high performance computing (HPC) environment. Finally this chapter point out the importance and standards of CUDA architecture.

scholarly journals High-performance computing in water resources hydrodynamics

2020 ◽  
Vol 22 (5) ◽  
pp. 1217-1235 ◽  
Author(s):  
M. Morales-Hernández ◽  
M. B. Sharif ◽  
S. Gangrade ◽  
T. T. Dullo ◽  
S.-C. Kao ◽  
...  
Keyword(s):  
Water Resources ◽  
High Performance Computing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Large Scale ◽  
Test Case ◽  
Processing Unit ◽  
Central Processing ◽  
Graphics Processing ◽  
Performance Computing

Abstract This work presents a vision of future water resources hydrodynamics codes that can fully utilize the strengths of modern high-performance computing (HPC). The advances to computing power, formerly driven by the improvement of central processing unit processors, now focus on parallel computing and, in particular, the use of graphics processing units (GPUs). However, this shift to a parallel framework requires refactoring the code to make efficient use of the data as well as changing even the nature of the algorithm that solves the system of equations. These concepts along with other features such as the precision for the computations, dry regions management, and input/output data are analyzed in this paper. A 2D multi-GPU flood code applied to a large-scale test case is used to corroborate our statements and ascertain the new challenges for the next-generation parallel water resources codes.

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

2009 ◽  
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 (CPU). The change in processor enables an order-of-magnitude reduction in run-time due to the higher performance of the GPU. 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 minutes on a cluster with four GPUs.

scholarly journals dadi.CUDA: Accelerating population genetic inference with Graphics Processing Units

2020 ◽  
Author(s):  
Ryan N Gutenkunst
Keyword(s):  
Graphics Processing Units ◽  
Population Genetic ◽  
Gpu Computing ◽  
Graphics Processing Unit ◽  
Demographic History ◽  
Processing Unit ◽  
Speed Increase ◽  
Population Genetic Inference ◽  
Computationally Intensive ◽  
Graphics Processing

Extracting insight from population genetic data often demands computationally intensive modeling. dadi is a popular program for fitting models of demographic history and natural selection to such data. Here, I show that running dadi on a Graphics Processing Unit (GPU) can speed computation by orders of magnitude compared to the CPU implementation, with minimal user burden. This speed increase enables the analysis of more complex models, which motivated the extension of dadi to four- and five-population models. Remarkably, dadi performs almost as well on inexpensive consumer-grade GPUs as on expensive server-grade GPUs. GPU computing thus offers large and accessible benefits to the community of dadi users. This functionality is available in dadi version 2.1.0.

scholarly journals DEVELOPING PARALLEL COMPUTING ALGORITHMS USING GPU’S TO DETERMINE OIL AND GAS RESERVES PRESENTED IN THE UPSTREAM (EXPLORATION) SECTOR

2020 ◽  
Author(s):  
Stefan Boodoo ◽  
Ajay Joshi
Keyword(s):  
High Performance ◽  
Oil And Gas ◽  
Gpu Computing ◽  
Graphics Processing Unit ◽  
Reservoir Rock ◽  
Processing Unit ◽  
Potential Wells ◽  
Central Processing ◽  
Rock Formations ◽  
Graphics Processing

Oil and Gas companies keep exploring every new possible method to increase the likelihood of finding a commercial hydrocarbon bearing prospect. Well logging generates gigabytes of data from various probes and sensors. After processing, a prospective reservoir will indicate areas of oil, gas, water and reservoir rock. Incorporating High Performance Computing (HPC) methodologies will allow for thousands of potential wells to be indicative of its hydrocarbon bearing potential. This study will present the use of the Graphics Processing Unit (GPU) computing as another method of analyzing probable reserves. Raw well log data from the Kansas Geological Society (1999-2018) forms the basis of the data analysis. Parallel algorithms are developed and make use of Nvidia’s Compute Unified Device Architecture (CUDA). The results gathered highlight a 5 times speedup using a Nvidia GeForce GT 330M GPU as compared to an Intel Core i7 740QM Central Processing Unit (CPU). The processed results display depth wise areas of shale and rock formations as well as water, oil and/or gas reserves.

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