Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit

Single Locus ◽

Future Research ◽

Processing Unit ◽

Forward Algorithm ◽

Vast Number ◽

Model Complex ◽

AbstractForward Wright-Fisher simulations are powerful in their ability to model complex demography and selection scenarios, but suffer from slow execution on the CPU, thus limiting their usefulness. The single-locus Wright-Fisher forward algorithm is, however, exceedingly parallelizable, with many steps which are so-called embarrassingly parallel, consisting of a vast number of individual computations that are all independent of each other and thus capable of being performed concurrently. The rise of modern Graphics Processing Units (GPUs) and programming languages designed to leverage the inherent parallel nature of these processors have allowed researchers to dramatically speed up many programs that have such high arithmetic intensity and intrinsic concurrency. The presented GPU Optimized Wright-Fisher simulation, or GO Fish for short, can be used to simulate arbitrary selection and demographic scenarios while running over 250-fold faster than its serial counterpart on the CPU. Even modest GPU hardware can achieve an impressive speedup of well over two orders of magnitude. With simulations so accelerated, one can not only do quick parametric bootstrapping of previously estimated parameters, but also use simulated results to calculate the likelihoods and summary statistics of demographic and selection models against real polymorphism data - all without restricting the demographic and selection scenarios that can be modeled or requiring approximations to the single-locus forward algorithm for efficiency. Further, as many of the parallel programming techniques used in this simulation can be applied to other computationally intensive algorithms important in population genetics, GO Fish serves as an exciting template for future research into accelerating computation in evolution. GO Fish is part of the Parallel PopGen Package available at: http://dl42.github.io/ParallelPopGen/

A lightweight approach to performance portability with targetDP

The International Journal of High Performance Computing Applications ◽

10.1177/1094342016682071 ◽

2016 ◽

Vol 32 (2) ◽

pp. 288-301

Author(s):

Alan Gray ◽

Kevin Stratford

Keyword(s):

Particle Physics ◽

Message Passing ◽

High Performance ◽

Large Scale ◽

Message Passing Interface ◽

Processing Unit ◽

Performance Portability ◽

Leading high performance computing systems achieve their status through use of highly parallel devices such as NVIDIA graphics processing units or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus separate lattice quantum chromodynamics particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with Message Passing Interface (MPI) to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and graphics processing unit-accelerated large scale supercomputers.

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

Classical and Quantum Gravity ◽

10.1088/1361-6382/ac4616 ◽

2021 ◽

Author(s):

Liam Dunn ◽

Patrick Clearwater ◽

Andrew Melatos ◽

Karl Wette

Keyword(s):

Gravitational Wave ◽

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.

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Emerging Research Surrounding Power Consumption and Performance Issues in Utility Computing ◽

Advanced Topics GPU Programming and CUDA Architecture

10.4018/978-1-4666-8853-7.ch008 ◽

2016 ◽

pp. 175-203

Author(s):

Mainak Adhikari ◽

Sukhendu Kar

Keyword(s):

High Performance ◽

Programming Model ◽

Direct Access ◽

Gpu Programming ◽

Processing Unit ◽

Computing Platform ◽

Cuda Architecture ◽

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.

Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management ◽

Data Streaming Processing Window Joined With Graphics Processing Units (GPUs)

10.4018/978-1-7998-3479-3.ch043 ◽

2021 ◽

pp. 602-623

Author(s):

Shen Lu ◽

Richard S. Segall

Keyword(s):

Big Data ◽

Data Streams ◽

Data Stream ◽

Large Scale ◽

Processing Unit ◽

Data Streaming ◽

Large Scale Data ◽

Big data is large-scale data and can be either discrete or continuous. This article entails research that discusses the continuous case of big data often called “data streaming.” More and more businesses will depend on being able to process and make decisions on streams of data. This article utilizes the algorithmic side of data stream processing often called “stream analytics” or “stream mining.” Data streaming Windows Join can be improved by using graphics processing unit (GPU) for higher performance computing. Data streams are generated by two independent threads: one thread can be used to generate Data Stream A, and the other thread can be used to generate Data Stream B. One would use a Windows Join thread to merge the two data streams, which is also the process of “Data Stream Window Join.” The Window Join process can be implemented in parallel that can efficiently improve the computing speed. Experiments are provided for Data Stream Window Joins using both static and dynamic data.

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

10.1101/2020.07.30.229336 ◽

2020 ◽

Author(s):

Ryan N Gutenkunst

Keyword(s):

Population Genetic ◽

Gpu Computing ◽

Demographic History ◽

Processing Unit ◽

Speed Increase ◽

Population Genetic Inference ◽

Computationally Intensive ◽

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.

Accelerating the RTTOV-7 IASI and AMSU-A radiative transfer models on graphics processing units: evaluating central processing unit/graphics processing unit-hybrid and pure-graphics processing unit approaches

Journal of Applied Remote Sensing ◽

10.1117/1.3658028 ◽

2011 ◽

Vol 5 (1) ◽

pp. 051503 ◽

Cited By ~ 4

Author(s):

Jarno Mielikainen

Keyword(s):

Radiative Transfer ◽

Central Processing Unit ◽

Processing Unit ◽

Central Processing ◽

Radiative Transfer Models ◽

Graphics Processing ◽

Transfer Models

Parallel SVD Algorithm for a Three-Diagonal Matrix on a Video Card Using the Nvidia CUDA Architecture

NaUKMA Research Papers Computer Science ◽

10.18523/2617-3808.2021.4.16-22 ◽

2021 ◽

Vol 4 ◽

pp. 16-22

Author(s):

Mykola Semylitko ◽

Gennadii Malaschonok

Keyword(s):

Computation Time ◽

Application Programming Interface ◽

General Purpose ◽

Diagonal Matrix ◽

Free Access ◽

Processing Unit ◽

Cuda Architecture ◽

SVD (Singular Value Decomposition) algorithm is used in recommendation systems, machine learning, image processing, and in various algorithms for working with matrices which can be very large and Big Data, so, given the peculiarities of this algorithm, it can be performed on a large number of computing threads that have only video cards.CUDA is a parallel computing platform and application programming interface model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit for general purpose processing – an approach termed GPGPU (general-purpose computing on graphics processing units). The GPU provides much higher instruction throughput and memory bandwidth than the CPU within a similar price and power envelope. Many applications leverage these higher capabilities to run faster on the GPU than on the CPU. Other computing devices, like FPGAs, are also very energy efficient, but they offer much less programming flexibility than GPUs.The developed modification uses the CUDA architecture, which is intended for a large number of simultaneous calculations, which allows to quickly process matrices of very large sizes. The algorithm of parallel SVD for a three-diagonal matrix based on the Givents rotation provides a high accuracy of calculations. Also the algorithm has a number of optimizations to work with memory and multiplication algorithms that can significantly reduce the computation time discarding empty iterations.This article proposes an approach that will reduce the computation time and, consequently, resources and costs. The developed algorithm can be used with the help of a simple and convenient API in C ++ and Java, as well as will be improved by using dynamic parallelism or parallelization of multiplication operations. Also the obtained results can be used by other developers for comparison, as all conditions of the research are described in detail, and the code is in free access.

Ray-based modeling and imaging in viscoelastic media using graphics processing units

Geophysics ◽

10.1190/geo2018-0510.1 ◽

2019 ◽

Vol 84 (5) ◽

pp. S425-S436

Author(s):

Martin Sarajaervi ◽

Henk Keers

Keyword(s):

Seismic Data ◽

Parallel Implementation ◽

Processing Unit ◽

Central Processing ◽

Imaging Results ◽

Viscoelastic Modeling ◽

Graphics Processing ◽

Complex Valued

In seismic data processing, the amplitude loss caused by attenuation should be taken into account. The basis for this is provided by a 3D attenuation model described by the quality factor [Formula: see text], which is used in viscoelastic modeling and imaging. We have accomplished viscoelastic modeling and imaging using ray theory and the ray-Born approximation. This makes it possible to take [Formula: see text] into account using complex-valued and frequency-dependent traveltimes. We have developed a unified parallel implementation for modeling and imaging in the frequency domain and carried out the numerical integration on a graphics processing unit. A central part of the implementation is an efficient technique for computing large integrals. We applied the integration method to the 3D SEG/EAGE overthrust model to generate synthetic seismograms and imaging results. The attenuation effects are accurately modeled in the seismograms and compensated for in the imaging algorithm. The results indicate a significant improvement in computational efficiency compared to a parallel central processing unit baseline.

A GPU-Based Wide-Band Radio Spectrometer

Publications of the Astronomical Society of Australia ◽

10.1017/pasa.2014.43 ◽

2014 ◽

Vol 31 ◽

Cited By ~ 11

Author(s):

Jayanth Chennamangalam ◽

Simon Scott ◽

Glenn Jones ◽

Hong Chen ◽

John Ford ◽

...

Keyword(s):

High Performance ◽

Wide Band ◽

Processing Unit ◽

Astronomical Instrumentation ◽

Online Data ◽

Polyphase Filter ◽

Polyphase Filter Bank ◽

AbstractThe graphics processing unit has become an integral part of astronomical instrumentation, enabling high-performance online data reduction and accelerated online signal processing. In this paper, we describe a wide-band reconfigurable spectrometer built using an off-the-shelf graphics processing unit card. This spectrometer, when configured as a polyphase filter bank, supports a dual-polarisation bandwidth of up to 1.1 GHz (or a single-polarisation bandwidth of up to 2.2 GHz) on the latest generation of graphics processing units. On the other hand, when configured as a direct fast Fourier transform, the spectrometer supports a dual-polarisation bandwidth of up to 1.4 GHz (or a single-polarisation bandwidth of up to 2.8 GHz).

ASYMPTOTIC PEAK UTILISATION IN HETEROGENEOUS PARALLEL CPU/GPU PIPELINES: A DECENTRALISED QUEUE MONITORING STRATEGY

Parallel Processing Letters ◽

10.1142/s0129626412400087 ◽

2012 ◽

Vol 22 (02) ◽

pp. 1240008 ◽

Cited By ~ 4

Author(s):

MICHAEL T. GARBA ◽

HORACIO GONZÁLEZ–VÉLEZ

Keyword(s):

General Purpose ◽

Numerical Linear Algebra ◽

Processing Unit ◽

Monitoring Strategy ◽

Memory Transfer ◽

Queue Monitoring ◽

Computational Resources ◽

Widespread heterogeneous parallelism is unavoidable given the emergence of General-Purpose computing on graphics processing units (GPGPU). The characteristics of a Graphics Processing Unit (GPU)—including significant memory transfer latency and complex performance characteristics—demand new approaches to ensuring that all available computational resources are efficiently utilised. This paper considers the simple case of a divisible workload based on widely-used numerical linear algebra routines and the challenges that prevent efficient use of all resources available to a naive SPMD application using the GPU as an accelerator. We suggest a possible queue monitoring strategy that facilitates resource usage with a view to balancing the CPU/GPU utilisation for applications that fit the pipeline parallel architectural pattern on heterogeneous multicore/multi-node CPU and GPU systems. We propose a stochastic allocation technique that may serve as a foundation for heuristic approaches to balancing CPU/GPU workloads.