Performance evaluation of GPU- and cluster-computing for parallelization of compute-intensive tasks

Watershed analysis, as a fundamental component of digital terrain analysis, is based on the Digital Elevation Model (DEM), which is a grid (raster) model of the Earth surface and topography. Watershed analysis consists of computationally and data intensive computing algorithms that need to be implemented by leveraging parallel and high-performance computing methods and techniques. In this paper, the Multiple Flow Direction (MFD) algorithm for watershed analysis is implemented and evaluated on multi-core Central Processing Units (CPU) and many-core Graphics Processing Units (GPU), which provides significant improvements in performance and energy usage. The implementation is based on NVIDIA CUDA (Compute Unified Device Architecture) implementation for GPU, as well as on OpenACC (Open ACCelerators), a parallel programming model, and a standard for parallel computing. Both phases of the MFD algorithm (i) iterative DEM preprocessing and (ii) iterative MFD algorithm, are parallelized and run over multi-core CPU and GPU. The evaluation of the proposed solutions is performed with respect to the execution time, energy consumption, and programming effort for algorithm parallelization for different sizes of input data. An experimental evaluation has shown not only the advantage of using OpenACC programming over CUDA programming in implementing the watershed analysis on a GPU in terms of performance, energy consumption, and programming effort, but also significant benefits in implementing it on the multi-core CPU.

The infrared (IR) signature of an aerial target due to the reflection of radiation from the Sun, the Earth’s surface and atmosphere plays an important role in aerial target detection and tracking. As the background radiation from the Earth’s surface, and atmosphere is distributed in the entire space and in a wide spectrum, it is time-consuming to obtain an aerial target’s reflected radiation. This problem is suitable for parallel implementation to run on multicore CPU or many-core GPU because the reflection of background radiation incident from different directions in each spectral wavelength can be calculated in parallel. We consider three different parallel approaches: 1) CPU implementation using OpenMP (open multiprocessing); 2) GPU implementation using OpenACC (open accelerators); and 3) GPU implementation using CUDA (compute unified device architecture). An NVIDIA K20c GPU (with 2496 cores) and two Intel Xeon E5-2690 CPU (with 8 cores each) are used in our experiment. Compared to their single-threaded CPU counterpart, speedups obtained by OpenMP, OpenACC, and CUDA implementations are 15x, 140x, 426x, respectively. The result shows that GPU implementations are promising in our problem.

Many geophysical problems are computationally expensive owing to their iterative nature or due to the programs processing to large datasets. Such problems are challenging and have to be approached with extreme caution because a wrong parameter selection will not only lead to wrong results but will also take up a lot of time. The Compute Unified Device Architecture (CUDA) introduced by NVIDIA has enabled programmers to execute tasks in parallel on a Graphics Processing Unit (GPU) using a high level language like C and C++. GPU's are massively parallel architectures with computing output several MFLOPS (10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> Floating Point Operations per second) higher than Central Processing Unit. They posses high memory bandwidth and low memory latency which makes it ideally suited for parallel computation. There are a number of geophysical processes which can benefit from reduced computing time. Iterative optimization procedures are one of them. We have implemented a CUDA version of the Particle Swarm Optimization (PSO) algorithm and used it to invert Self Potential, Magnetic and Resistivity data. The CUDA version of the algorithm was compared to an efficient CPU implementation of the same. We observed significant speed up compared to a CPU only version and the results of the CUDA version were as good as the CPU version.

Turbo Similarity Searching (TSS) is a two phases searching procedure that has been proven by previous works as one of the best searching method on chemical databases. TSS however consumes lots of computation time due to the number of searches and fusion carried out in the second phase of its procedure. Hence, TSS would not be able to cater the increase in the chemical database size due to this limitation. With the emergence of the parallel technology, this research looks into accelerating TSS on the widely-used many-cores i.e. the Graphics Processing Unit (GPU) and multi-cores platform. This would not only solve the computational time issue but also the cost as GPUs can be obtained at a lower cost. Hence, the implementation of TSS will help the medicinal chemist to execute the virtual screening in an accurate and fast manner. This study investigates the best possible method to parallelize TSS via experimentation of three parallel designs; two designs were implemented on GPU platform using the Compute Unified Device Architecture (CUDA) API namely CUDA 1 and CUDA 2 whilst one design was implemented on multi-core platform using OpenMP API. The CUDA 1 design had shown tremendous speedup and low GPU-memory utilization as compared to CUDA 2 design. In general observation, the parallel CUDA 1 was 131 times faster than sequential and 51 times faster than parallel OpenMP. This leads to the conclusion that CUDA 1 design as the best parallel design for TSS.

Summary form only given. Numerical analysis of electromagnetic scattering has been widely studied in the past. The method of moments (MoM) is suitable to simulate scattering from an object in an open region. In order to solve problems with complicated geometries and materials, the finite element method (FEM) is preferred. However, when solving unbounded problems with FEM, the use of absorbing boundary conditions or perfectly matched layers will result in truncation errors. To combine the advantages of both MoM and FEM, the finite element-boundary integral (FE-BI) method was developed and widely used (J. M. Jin, The Finite Element Method in Electromagnetics, Second Edition. New York: Wiley, 2002). This algorithm is not only able to deal with highly complicated geometry and material, but also able to eliminate the truncation error. However, since the MoM has O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2)</sup> computational and storage complexities, the computational cost of the BI-related algorithm is still prohibitively high when solving large-scale scattering problems. To alleviate this problem, the multilevel fast multipole algorithm (MLFMA) has been incorporated into the FE-BI method (J. Liu and J. M. Jin, IEEE Trans. Antennas Propag., vol. 51, pp.1157-1167, Jun. 2003). Another important technique to accelerate the computation is to resort to parallel computers. Recently, the graphics processing unit (GPU), as one of many-core computing systems, has received intensive attention from the scientific computing community due to its low price, a massive number of processors, and the resulting high computational efficiency. In this work, we discuss the GPU implementation of the FE-BI algorithm for solving electromagnetic scattering problems. First, the assembly of the BI-related matrix is parallelized on GPU by NVIDIA's compute unified device architecture (CUDA). A massive number of threads are assigned to carry out the assembly, and each thread computes one element in the BI-related matrix. Second, both the direct and iterative solvers are implemented on GPU. For the direct solver, the Intel's math kernel library (MKL) is used for LU decomposition, and the CUSPARSE (a set of basic linear algebra subroutines used for handling sparse matrices) is employed to speedup forward and backward substitutions. For the iterative solver, the high performance CUDA Basic Linear Algebra Subprograms (BLAS) is utilized for the matrix-vector multiplication and the vector-vector multiplication. Extensive numerical experiments are conducted to validate the implementation and demonstrate the achievable computational speedup.

Background subtraction is an important problem in computer vision and is a fundamental task for many applications. In the past, background subtraction has been limited by the amount of computing power available. The task was performed on small frames and, in the case of adaptive algorithms, with relatively small models to achieve real-time performance. With the introduction of multi- and many-core chip-multiprocessors (CMP), more computing resources are available to handle this important task. The advent of specialized CMP, such as NVIDIA's Compute Unified Device Architecture (CUDA) and IBM's Cell Broadband Engine Architecture (CBEA), provides new opportunities to accelerate real-time video applications. In this paper, we evaluate the acceleration of background subtraction with these two different chip-multiprocessor (CMP) architectures (CUDA and CBEA), such that larger image frames can be processed with more models while still achieving real-time performance. Our analysis results show impressive performance improvement over a baseline implementation that uses a multi-threaded dual-core CPU. Specifically, the CUDA implementation and CBEA implementation can achieve up to 17.82X and 2.77X improvement, respectively.

Many-core framework, represented by Graphic Processing Unit (GPU), has been becoming more popular in scientific computing and data analysis, and Compute Unified Device Architecture (CUDA) allows developers to carry out computation on GPUs more conveniently. Matrix operation is one of the most important application scenarios of GPU computation and CUDA, widely appearing in domains like graph applications and artificial intelligence. This paper focuses on accelerating matrix power operation (MPO) process, which appears in many engineering problems and may be time-consuming when the scale of matrixes is large, with GPUs and CUDA. In this paper, three techniques are used to speed up the MPO process: matrix parallel reduction, matrix parallel multiplication and CUDA dynamic parallelism. The experiments were carried out on NVIDIA RTX 2080 Super and Intel(R) Core(TM) i7-10750H. The code is compiled and executed in Visual Studio 2019 community, the GPU driver version is 461.40, the CUDA version is 11.1, and the NVCC version is 11.1.74. With all three techniques, the MPO process can get hundreds of times speedup compared with the sequential multiplication process executed by Eigen library, even though the code of algorithm is hardly optimized. Moreover, with the number of power increasing, the efficiency of algorithm can be even higher because of the relatively fixed time consumed in data transmission and GPU memory allocation.

Based on the principle of QR loop iterations, this paper implements a parallel algorithm based on the hardware of GPU (Graphic Process Unit) by using routines from CUDA (Computer Unified Device Architecture) to find the eigenvalues of general matrices. CPU and GPU computing card form a client-server computing framework. Here, CPU can be regarded as a client and GPU card is considered as a computing server. For the experiment environment, this paper chooses a GPU card with the model of NVIDIA GeForce GTX460 as a server side and a CPU with the model of Intel Core i5-760 quad-core as a client side. Win7 64-bit is selected as the operating system. The parallel implementation consists of two parts:PA H and PA QR. PA H is a procedure that transforms a matrix A into the Hessenberg matrix B. PA QR is the actual parallel algorithm of the QR iterations that is imposed on the Hessenberg matrix B for finding eigenvalues of the general matrix A. The speedup ratio of the proposed algorithm is jarless when the number of iterations becomes greater. The experimental results show that the parallel implementation with CUDA on GPU only makes use of less running time than traditional sequential algorithms. The speedup ratio of PA H is between 1.79 and 7.81. The speedup ratio of PA QR is between 3.24 and 118.9. Especially, when the order of general matrix equals 8192, the amount of iterations becomes 10000. The speedup ratio of the PA-H and the PA QR can run up to 7.81 and 118.9 respectively.

The small slop approximation (SSA) is an accurate method to calculate the electromagnetic (EM) scattering properties of rough surfaces. However, its computational complexity restricts its application to smaller domains and there is always the need for speedup in very large cases using pure central processing units (CPUs) hardware. With the development of graphics processing units (GPUs), more processors are dedicated to perform independent calculations. In addition, NVIDIA introduced a parallel computing platform, compute unified device architecture (CUDA), which provides researchers an easy way to use processors on GPU. To calculate EM scattering properties on GPU, we reformulate the SSA method with CUDA to take advantage of GPU threads. Because each thread executes synchronously and deals with a corresponding point data of rough surface, the CUDA-based SSA method calculates faster than the pure-CPU equivalent. To overcome memory limitations, the data of large rough surface are stored on hard disk. Moreover, a subsidiary thread is used to deal with the process of data transmission between the memory and the hard disk and reduce transmitting time further. The factors, block size, data transfers, and register, are also discussed in the optimization of the CUDA application. Test cases running on a NVIDIA GTX 460 GPU indicate that two orders of magnitude speedup, including file input and output, is obtained with our new formulation.

During the last decades, explicit approximate inverse preconditioning methods have been used for efficiently solving sparse linear systems on multiprocessor systems. The effectiveness of explicit approximate inverse preconditioning schemes relies on the use of efficient preconditioners that are close approximants to the coefficient matrix and are fast to compute in parallel. A new parallel computational technique is proposed for the parallelization of the explicit preconditioned conjugate gradient type method on a Graphics Processing Unit (GPU). The proposed parallel methods have been implemented using Compute Unified Device Architecture (CUDA) developed by NVIDIA. The inherently parallel operations between vectors and matrices involved in the explicit preconditioned biconjugate conjugate gradient type schemes exhibit significant amounts of loop-level parallelism because of the matrix–vector and the vector–vector products that can lead to high performance gain on the GPU systems, specifically designed for such computations. Finally, numerical results for the performance of the explicit preconditioned biconjugate conjugate gradient type method for solving characteristic two dimensional boundary value problems, using the finite difference method, on a massive multiprocessor interface on a GPU are presented. The CUDA implementation issues of the proposed method are also discussed.

Most Computational Fluid Dynamics (CFD) applications solve the Naiver-Stokes equations using various discretization methods like Finite Difference Method (FDM), Finite Element Method (FEM), Finite Volume Method (FVM), etc. The most compute-intensive process in CFD algorithms is the pressure Poisson solver. Solving the pressure Poisson equation requires the solution of a set of linear simultaneous equations. As the problem size/complexity increases, so does the effort and time invested in solving the equations. This study presents fast and robust iterative solvers and the acceleration of the same using the Graphics Processing Unit (GPU) architecture. Serial versions of the codes are implemented in the C language and the parallelization on GPU is achieved using Compute Unified Device Architecture (CUDA). For this study, a two-dimensional heat conduction problem is considered. FDM is used for discretization. Iterative solvers Conjugate Gradient and Multi Grid methods have been implemented in serial and parallel. The performance enhancement with single GPU and CPU system over a single CPU in terms of computing time is reported. It is seen that multigrid algorithms are superior in convergence and nearly 13 times speed-up has been obtained through GPU acceleration using CUDA, for a grid size of 2048 X 2048. The tests have been done on a cluster with Intel(R) Xeon(R) CPU E5-2670 CPU and NVIDIA K10 GPU.

Purpose: For microdosimetric calculation event-by-event Monte Carlo (MC) simulation is considered the most accurate, but it is very time-consuming. In this work we present an event-by-event MC simulation of low energy electron and proton for accelerated microdosimetric MC simulations on a graphic processing unit (GPU). Methods: The MC simulation of particle was implemented in C and executed on a multi-core CPU, and a commercially available general purpose GPU using the compute unified device architecture (CUDA). Additionally, a hybrid implementation scheme was realized by employing OpenMP and CUDA so that both GPU and multi-core CPU were utilized simultaneously. The two implementation schemes have been tested and compared with the sequential single threaded MC simulation on the CPU. A calculation time comparison was established on the speed-up for a set of benchmarking cases of electron and proton. Results: Dosimetric results were obtained with both the parallel and serial MC codes. A GPU over CPU speed-up of 67.2 and 19.2 times was achieved for 300 eV and 2 keV electron tracks respectively. For proton tracks, the GPU-based code was approximately 5 times faster than the CPU-single-thread code. By incorporating a multi-core CPU and running the MC code simultaneously on the GPU and CPU, an increase of 2%-7% and 20% in the speedup was noticed for electrons and protons tracks respectively. A good dosimetric agreement between the CPU- and GPU-based MC methods was observed for both electrons and proton. Conclusions: A GPU-based MC method for microdosimetric calculations of low energy electron and proton has been presented. The results indicate the capability of our GPU-based implementation for accelerated MC simulations of both electron and proton without loss of accuracy. Lastly, the potential of a hybrid approach by utilizing simultaneously a GPU and a multi-core CPU for further acceleration of MC microdosimetric calculations has been demonstrated.

<strong class="journal-contentHeaderColor">Abstract.</strong> With semiconductor technology gradually approaching its physical and thermal limits, Graphics processing unit (GPU) is becoming an attractive solution in many scientific applications due to their high performance. This paper presents an application of GPU accelerators in air quality model. We endeavor to demonstrate an approach that runs a PPM solver of horizontal advection (HADVPPM) for air quality model CAMx on GPU clusters. Specifically, we first convert the HADVPPM to a new Compute Unified Device Architecture C (CUDA C) code to make it computable on the GPU (GPU-HADVPPM). Then, a series of optimization measures are taken, including reducing the CPU-GPU communication frequency, increasing the size of data computation on GPU, optimizing the GPU memory access, and using thread and block indices in order to improve the overall computing performance of CAMx model coupled with GPU-HADVPPM (named as CAMx-CUDA model). Finally, a heterogeneous, hybrid programming paradigm is presented and utilized with the GPU-HADVPPM on GPU clusters with Massage Passing Interface (MPI) and CUDA. Offline experiment results show that running GPU-HADVPPM on one NVIDIA Tesla K40m and NVIDIA Tesla V100 GPU can achieve up to 845.4x and 1113.6x acceleration. By implementing a series of optimization schemes, the CAMx-CUDA model resulted in a 29.0x and 128.4x improvement in computational efficiency using a GPU accelerator card on a K40m and V100 cluster, respectively. In terms of the single-module computational efficiency of GPU-HADVPPM, it can achieve 1.3x and 19.4x speedup on NVIDIA Tesla K40m GPU and NVIDA Tesla V100 GPU respectively. The multi-GPU acceleration algorithm enables 4.5x speedup with 8 CPU cores and 8 GPU accelerators on V100 cluster.

<strong class="journal-contentHeaderColor">Abstract.</strong> With semiconductor technology gradually approaching its physical and thermal limits, Graphics processing unit (GPU) is becoming an attractive solution in many scientific applications due to their high performance. This paper presents an application of GPU accelerators in air quality model. We endeavor to demonstrate an approach that runs a PPM solver of horizontal advection (HADVPPM) for air quality model CAMx on GPU clusters. Specifically, we first convert the HADVPPM to a new Compute Unified Device Architecture C (CUDA C) code to make it computable on the GPU (GPU-HADVPPM). Then, a series of optimization measures are taken, including reducing the CPU-GPU communication frequency, increasing the size of data computation on GPU, optimizing the GPU memory access, and using thread and block indices in order to improve the overall computing performance of CAMx model coupled with GPU-HADVPPM (named as CAMx-CUDA model). Finally, a heterogeneous, hybrid programming paradigm is presented and utilized with the GPU-HADVPPM on GPU clusters with Massage Passing Interface (MPI) and CUDA. Offline experiment results show that running GPU-HADVPPM on one NVIDIA Tesla K40m and NVIDIA Tesla V100 GPU can achieve up to 845.4x and 1113.6x acceleration. By implementing a series of optimization schemes, the CAMx-CUDA model resulted in a 29.0x and 128.4x improvement in computational efficiency using a GPU accelerator card on a K40m and V100 cluster, respectively. In terms of the single-module computational efficiency of GPU-HADVPPM, it can achieve 1.3x and 19.4x speedup on NVIDIA Tesla K40m GPU and NVIDA Tesla V100 GPU respectively. The multi-GPU acceleration algorithm enables 4.5x speedup with 8 CPU cores and 8 GPU accelerators on V100 cluster.

This paper proposes a parallel ant colony optimization (ACO) for solving quadratic assignment problems (QAPs) on a graphics processing unit (GPU) by combining fast, 2-opt local search in compute unified device architecture (CUDA). In 2-opt for QAP, 2-opt moves can be divided into two groups based on computing cost. In one group, the computing cost is O(1) and in the other group, the computing cost is O(n). We compute these groups of 2-opt moves in parallel by assigning the computations to threads of CUDA. In this assignment, we propose an efficient method that can reduce disabling time in each thread of CUDA. The results show GPU computation with 2-opt produces a speedup of ×24.6 on average, compared to computation with CPU.