Performance Analysis of Different Iterative Solvers Parallelized On GPU Architecture

This paper presents a parallel system capable of accelerating biological sequence alignment on the graphics processing unit (GPU) grid. The GPU grid in this paper is a desktop grid system that utilizes idle GPUs and CPUs in the office and home. Our parallel implementation employs a master-worker paradigm to accelerate an OpenGL-based algorithm that runs on a single GPU. We integrate this implementation into a screensaver-based grid system that detects idle resources on which the alignment code can run. We also show some experimental results comparing our implementation with three different implementations running on a single GPU, a single CPU, or multiple CPUs. As a result, we find that a single non-dedicated GPU can provide us almost the same throughput as two dedicated CPUs in our laboratory environment, where GPU-equipped machines are ordinarily used to develop GPU applications. In a dedicated environment, the GPU-accelerated code achieves five times higher throughput than the CPU-based code. Furthermore, a linear speedup of 30.7X is observed on a 32-node cluster of dedicated GPUs. We also implement a compute unified device architecture (CUDA) based algorithm to demonstrate further acceleration.

Performance models for asynchronous data transfers on consumer Graphics Processing Units

We present a spectral crystal plasticity (CP) solver for graphics processing unit (GPU) architectures that achieves a tenfold increase in efficiency over prior GPU solvers. The approach makes use of a database containing a spectral decomposition of CP simulations performed using a conventional iterative solver over a parameter space of crystal orientations and applied velocity gradients. The key improvements in efficiency come from reducing global memory transactions, exposing more instruction-level parallelism, reducing integer instructions and performing fast range reductions on trigonometric arguments. The scheme also makes more efficient use of memory than prior work, allowing for larger problems to be solved on a single GPU. We illustrate these improvements with a simulation of 390 million crystal grains on a consumer-grade GPU, which executes at a rate of 2.72 s per strain step.

<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.

GPU acceleration of conjugate gradient method obtaining Green's function for transport-property calculation

Programmable graphics processing units (GPUs) nowadays offer very high performance computing power at relatively low hardware cost and power consumption. In this paper, we present the implementation of the dynamics routine of the HIRLAM weather forecast model on the NVIDIA GeForce 9800 GX2 GPU card using the Compute Unified Device Architecture (CUDA) as parallel programming model. We converted the original Fortran to C and CUDA by hand, straightforwardly, without much concern about optimization. On a single GPU, we observe speed-ups by an order of magnitude over our hosting CPU (Intel quad core, 1998 MHz). This includes the relatively very costly copying of data between GPU and CPU memories. Calculation times proper decreased by a factor of 2000. A single GPU, however, has not enough memory for practical use. Therefore, we investigated a parallel implementation on 4 GPUs. We found a parallel speed-up of 3.6, which is not very promising if memory limitations force the use of many GPUs in parallel. We discuss several options to solve this issue.

A direct simulation Monte Carlo (DSMC) method is implemented on graphical processing unit (GPU) architecture to simulate the deposition phenomena that occur on a glass panel during modern organic light emitting diodes (OLED) manufacturing processes with a huge number of simulated particles because a fully three-dimensional (3D) particle simulation is required to accurately predict deposition thickness and uniformity on the substrate in a low-pressure environment. To examine the accuracy and efficiency of the newly developed DSMC code running on GPU architecture, verification and validation tests were performed by solving several benchmarking problems. Under three conditions which have been applied in previous OLED deposition process test experiments, the new DSMC model successfully simulates the transport of organic material evaporated from the heated nozzle array and its deposition onto a glass panel inside a low-pressure chamber. The uniformity and normalized thickness between the DSMC and experimental results for the three test cases are then compared. The normalized thickness of the organic material deposited to the large glass panel calculated from the DSMC is found to have an error of less than 4%, and the uniformity values calculated from the absolute deposition thickness show good agreement with those obtained from experimental data in all test cases.

With the development of information technology and the increasing demand for data processing, the serial mode of the central processing unit (CPU) is difficult to efficiently transmit large-scale spatiotemporal data, and the processing effect for high-resolution images is not good. This paper designed a high-resolution image processing and spatiotemporal data transmission system based on graphics processing unit (GPU) acceleration to improve the processing efficiency of large-scale spatiotemporal data. In this paper, traffic spatiotemporal data was taken as an example for analysis. Large-scale traffic image data was collected by road monitoring equipment, and image compression was performed on the collected image. Fourier transform was used to eliminate image data redundancy, and GPU-accelerated parallel processing was used to achieve fast image defogging and data transmission. This paper selected 2TB of traffic spatiotemporal data with image resolutions of 540P, 720P, 1080P, 1440P, and 2160P. GPU acceleration was performed using the Compute Unified Device Architecture (CUDA). In images with a resolution of 2160P, the processing time for CPU and GPU acceleration was 2900ms and 28ms, respectively, with an acceleration ratio of 103.6. A high-resolution image processing and spatiotemporal data transmission system based on GPU acceleration can improve the efficiency of traffic spatiotemporal data processing and have excellent concurrent processing capabilities.

The density peak (DP) algorithm has been widely used in scientific research due to its novel and effective peak density-based clustering approach. However, the DP algorithm uses each pair of data points several times when determining cluster centers, yielding high computational complexity. In this paper, we focus on accelerating the time-consuming density peaks algorithm with a graphics processing unit (GPU). We analyze the principle of the algorithm to locate its computational bottlenecks, and evaluate its potential for parallelism. In light of our analysis, we propose an efficient parallel DP algorithm targeting on a GPU architecture and implement this parallel method with compute unified device architecture (CUDA), called the ‘CUDA-DP platform’. Specifically, we use shared memory to improve data locality, which reduces the amount of global memory access. To exploit the coalescing accessing mechanism of GPU, we convert the data structure of the CUDA-DP program from array of structures to structure of arrays. In addition, we introduce a binary search-and-sampling method to avoid sorting a large array. The results of the experiment show that CUDA-DP can achieve a 45-fold acceleration when compared to the central processing unit based density peaks implementation.

List mode based image reconstruction is continuously gaining ground in PET imaging. The centerpiece of this reconstruction is the ray projector which unfortunately also represents the most time consuming part of the reconstruction process. Graphics processing units (GPU) represent today a cheap alternative to getting high computation power in medical imaging. However, only few GPU adapted methods have been proposed within the PET image reconstruction context, especially concerning the ray projector. In this work we propose and evaluate a new projector called DDA-ELL, which has the advantage of being applicable to both CPU and GPU architectures. The proposed projector was incorporated and evaluated within the 3D-OPLEM reconstruction algorithm implemented on both CPU and GPU architectures. Quantitative and qualitative performance evaluation was performed using simulated phantom and clinical datasets. We have shown that the proposed ray projector reconstructed images lead to improved noise and contrast characteristics compared to the widely used iterative Siddon approach. The speedup reached by the CPU version of our method compared to the iterative Siddon method was 11, while the GPU version was nearly 140 times faster.

The graphics processing unit (GPU) became an undoubtedly important computing engine for high-performance computing. With massive parallelism and easy programmability, GPU has been quickly adopted by various emerging computing domains including gaming, artificial intelligence, security, virtual reality, and so on. With its huge success in the market, GPU execution and its architecture became one of the essential topics in parallel computing today. The goal of this chapter is to provide readers with a basic understanding of GPU architecture and its programming model. This chapter explores the historical background of current GPU architecture, basics of various programming interfaces, core architecture components such as shader pipeline, schedulers and memories that support SIMT execution, various types of GPU device memories and their performance characteristics, and some examples of optimal data mapping to memories. Several recent studies are also discussed that helped advance the GPU architecture from the perspectives of performance, energy efficiency, and reliability.

Graphics Processing Units (GPUs) are massively parallel, many-core processors with tremendous computational power and very high memory bandwidth. With the advent of general purpose programming models such as NVIDIA's CUDA and the new standard OpenCL, general purpose programming using GPUs (GPGPU) has become very popular. However, the GPU architecture and programming model have brought along with it many new challenges and opportunities for compiler optimizations. One such classical optimization is loop unrolling. Current GPU compilers perform limited loop unrolling. In this paper, we attempt to understand the impact of loop unrolling on GPGPU programs. We develop a semi-automatic, compile-time approach for identifying optimal unroll factors for suitable loops in GPGPU programs. In addition, we propose techniques for reducing the number of unroll factors evaluated, based on the characteristics of the program being compiled and the device being compiled to. We use these techniques to evaluate the effect of loop unrolling on a range of GPGPU programs and show that we correctly identify the optimal unroll factors. The optimized versions run up to 70 percent faster than the unoptimized versions.

In this study, an in-house higher-order, high resolution implicit CFD solver for Favre-averaged Navier-Stokes equation with two-equation turbulence model, which including fourth-order MUSCL TVD scheme for Right-Hand-Side and LU-SGS-GE scheme for Left-Hand-Side, is implemented to run on a single Graphic Process Unit (GPU) using the Compute Unified Device Architecture (CUDA). The partly parallel model for implicit scheme is introduced and we have got a good acceleration using this partly parallel model. The case of VKI 59 is used to test the accelerations. Results obtained on a single GPU (NVDIA GeForce 560Ti, Memory: DDR5 1G 2004 Mhz) are compared with the results obtained on the single CPU (Intel i5 2.8–3.0 GHz, Memory: DDR3 8G 800 Mhz). The speed-up is about 25. The fourth-order MUSCL TVD used in the code is extended to the simulation of multi-species, and also speed-up by using GPU. The case of a single stage turbine with hot-spot inlet boundary is used to test the code with multi-species scheme using GPU. Furthermore, in the study, the shared memory is used to speed-up the code, which is introduced in details in the paper. In the end, the influence factors of the acceleration are discussed.

Direct numerical simulations of supersonic compression-expansion slope with a multi-GPU parallel algorithm