Study of Parallel Programming Models on Computer Clusters with Accelerators

In order to reach exascale computing capability, accelerators have become a crucial part in developing supercomputers. This work examines the potential of two latest acceleration technologies, Intel Many Integrated Core (MIC) Architecture and Graphics Processing Units (GPUs). This thesis applies three benchmarks under 3 different configurations, MPI+CPU, MPI+GPU, and MPI+MIC. The benchmarks include intensely communicating application, loosely communicating application, and embarrassingly parallel application. This thesis also carries out a detailed study on the scalability and performance of MIC processors under two programming models, i.e., offload model and native model, on the Beacon computer cluster. According to different benchmarks, the results demonstrate different performance and scalability between GPU and MIC. (1) For embarrassingly parallel case, GPU-based parallel implementation on Keeneland computer cluster has a better performance than other accelerators. However, MIC-based parallel implementation shows a better scalability than the implementation on GPU. The performances of native model and offload model on MIC are very close. (2) For loosely communicating case, the performances on GPU and MIC are very close. The MIC-based parallel implementation still demonstrates a strong scalability when using 120 MIC processors in computation. (3) For the intensely communicating case, the MPI implementations on CPUs and GPUs both have a strong scalability. GPUs can consistently outperform other accelerators. However, the MIC-based implementation cannot scale quite well. The performance of different models on MIC is different from the performance of embarrassingly parallel case. Native model can consistently outperform the offload model by ~10 times. And there is not much performance gain when allocating more MIC processors. The increase of communication cost will offset the performance gain from the reduced workload on each MIC core. This work also tests the performance capabilities and scalability by changing the number of threads on each MIC card form 10 to 60. When using different number of threads for the intensely communicating case, it shows different capabilities of the MIC based offload model. The scalability can hold when the number of threads increases from 10 to 30, and the computation time reduces with a smaller rate from 30 threads to 50 threads. When using 60 threads, the computation time will increase. The reason is that the communication overhead will offset the performance gain when 60 threads are deployed on a single MIC card

Efficient multitemporal change detection techniques for hyperspectral images on GPU

Hyperspectral images contain hundreds of reflectance values for each pixel. Detecting regions of change in multiple hyperspectral images of the same scene taken at different times is of widespread interest for a large number of applications. For remote sensing, in particular, a very common application is land-cover analysis. The high dimensionality of the hyperspectral images makes the development of computationally efficient processing schemes critical. This thesis focuses on the development of change detection approaches at object level, based on supervised direct multidate classification, for hyperspectral datasets. The proposed approaches improve the accuracy of current state of the art algorithms and their projection onto Graphics Processing Units (GPUs) allows their execution in real-time scenarios

Digital image blurring-deblurring process for improved storage and transmission

This paper investigates the likelihood of using a blurring-deblurring process as a pre-post-processing step in standard image reconstruction and compression. As such, the paper relates to image coding and compression systems whereby an original image can be transmitted or stored in a coded and compressed representation which renders it blurred and degraded. The compressibility of an image increases with the blurring, whereby the relation between compression ratio (CR) and the blurring scale is approximately linear. Hence, by pre-processing and blurring an image before compression, the CR will increase accordingly. The function or process tested here for blurring-deblurring an image is based on a pixel group processing, whereby the original image is sampled at sub-pixel levels. Since the sub-pixel shifts between each pixel group sampling are known exactly,a blurred image is created which can be shown to contain the details of the original image and thereby restored or reconstructed by reversing the blurring process.The complementary effects of increased CR are examined in terms of coding/decoding execution times and the quality of the reconstructed images

Mobile robot tank with GPU assistance

Robotic research has been costly and tremendously time consuming; this is due to the cost of sensors, motors, computational unit, physical construction, and fabrication. There are also a lot of man hours clocked in for algorithm design, software development, debugging and optimizing. Robotic vision input usually consists of 2 or more color cameras to construct a 3D virtual space. Additional cameras can also be added to enrich the detailed virtual 3D environment, however, the computational complexity increases when more cameras are added. This is due to not only the additional processing power that is required running the software but also the complexity of stitching multiple cameras together to form a sensor. Another method of creating the 3D virtual space is the utilization of range finder sensors. These types of sensors are usually relatively expensive and still require complex algorithms for calibration and correlation to real life distances. Sensing of a robot position can be facilitated by the addition of accelerometers and gyroscope sensors. A significant robotic design is robot interaction. One type of interaction is through verbal exchange. Such interaction requires an audio input receiver and transmitter on the robot. In order to achieve acceptable recognitions, different types of audio receivers may be implemented and many receivers are required to be deployed. Depending on the environment, noise cancellation hardware and software may be needed to enhance the verbal interaction performance. In this thesis different audio sensors are evaluated and implemented with Microsoft Speech Platform. Any robotic research requires a proper control algorithm and logic process. As a result, the majority of these control algorithms rely heavily on mathematics. High performance computational processing power is needed to process all the raw data in real-time. However, any high performance computation proportionally consumes more energy, so to conserve battery life on the robot, many robotic researchers implement remote computation by processing the raw data remotely. This implementation has one major drawback: in order to transmit raw data remotely, a robust communication infrastructure is needed. Without a robust communication the robot will suffers in failures due to the sheer amount of raw data in communication. I am proposing a solution to the computation problem by harvesting the General Purpose Graphic Processing Unit (GPU)’s computational power for complex mathematical raw data processing. This approach utilizes many-cores parallelism for multithreading real-time computation with a minimum of 10x the computational flops of traditional Central Processing Unit (CPU). By shifting the computation on the GPU the entire computation will be done locally on the robot itself to eliminate the need of any external communication system for remote data processing. While the GPU is used to perform image processing for the robot, the CPU is allowed to dedicate all of its processing power to run the other functions of the robot. Computer vision has been an interesting topic for a while; it utilizes complex mathematical techniques and algorithms in an attempt to achieve image processing in real-time. Open Source Computer Vision Library (OpenCV) is a library consisting of pre-programed image processing functions for several different languages, developed by Intel. This library greatly reduces the amount of development time. Microsoft Kinect for XBOX has all the sensors mentioned above in a single convenient package with first party SDK for software development. I perform the robotic implementation utilizing Microsoft Kinect for XBOX as the primary sensor, OpenCV for image processing functions and NVidia GPU to off-load complex mathematical raw data processing for the robot. This thesis’s objective is to develop and implement a low cost autonomous robot tank with Microsoft Kinect for XBOX as the primary sensor, a custom onboard Small Form Factor (SFF) High Performance Computer (HPC) with NVidia GPU assistance for the primary computation, and OpenCV library for image processing functions

Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs

The objective of this thesis is to develop efficient schemes for spectral-spatial n-dimensional image classification. By efficient schemes, we mean schemes that produce good classification results in terms of accuracy, as well as schemes that can be executed in real-time on low-cost computing infrastructures, such as the Graphics Processing Units (GPUs) shipped in personal computers. The n-dimensional images include images with two and three dimensions, such as images coming from the medical domain, and also images ranging from ten to hundreds of dimensions, such as the multiand hyperspectral images acquired in remote sensing. In image analysis, classification is a regularly used method for information retrieval in areas such as medical diagnosis, surveillance, manufacturing and remote sensing, among others. In addition, as the hyperspectral images have been widely available in recent years owing to the reduction in the size and cost of the sensors, the number of applications at lab scale, such as food quality control, art forgery detection, disease diagnosis and forensics has also increased. Although there are many spectral-spatial classification schemes, most are computationally inefficient in terms of execution time. In addition, the need for efficient computation on low-cost computing infrastructures is increasing in line with the incorporation of technology into everyday applications. In this thesis we have proposed two spectral-spatial classification schemes: one based on segmentation and other based on wavelets and mathematical morphology. These schemes were designed with the aim of producing good classification results and they perform better than other schemes found in the literature based on segmentation and mathematical morphology in terms of accuracy. Additionally, it was necessary to develop techniques and strategies for efficient GPU computing, for example, a block–asynchronous strategy, resulting in an efficient implementation on GPU of the aforementioned spectral-spatial classification schemes. The optimal GPU parameters were analyzed and different data partitioning and thread block arrangements were studied to exploit the GPU resources. The results show that the GPU is an adequate computing platform for on-board processing of hyperspectral information

Visual Analysis Algorithms for Embedded Systems

The main contribution of this thesis is the design and development of an optimized framework to realize the deep neural classifiers on the embedded platforms. Deep convolutional networks exhibit unmatched performance in image classification. However, these deep classifiers demand huge computational power and memory storage. That is an issue on embedded devices due to limited onboard resources. The computational demand of neural networks mainly stems from the convolutional layers. A significant improvement in performance can be obtained by reducing the computational complexity of these convolutional layers, making them realizable on embedded platforms. In this thesis, we proposed a CUDA (Compute Unified Device Architecture)-based accelerated scheme to realize the deep architectures on the embedded platforms by exploiting the already trained networks. All required functions and layers to replicate the trained neural networks were implemented and accelerated using concurrent resources of embedded GPU. Performance of our CUDA-based proposed scheme was significantly improved by performing convolutions in the transform domain. This matrix multiplication based convolution was also compared with the traditional approach to analyze the improvement in inference performance. The second part of this thesis focused on the optimization of the proposed framework. The flow of our CUDA-based framework was optimized using unified memory scheme and hardware-dependent utilization of computational resources. The proposed flow was evaluated over three different image classification networks on Jetson TX1 embedded board and Nvidia Shield K1 tablet. The performance of proposed GPU-only flow was compared with its sequential and heterogeneous versions. The results showed that the proposed scheme brought the higher performance and enabled the real-time image classification on the embedded platforms with lesser storage requirements. These results motivated us towards the realization of useful real-time classification and recognition problems on the embedded platforms. Finally, we utilized the proposed framework to realize the neural network-based automatic license plate recognition (ALPR) system on a mobile platform. This highly-precise and computationally demanding system was deployed by simplifying the flow of trained deep architecture developed for powerful desktop and server environments. A comparative analysis of computational complexity, recognition accuracy and inference performance was performed

Advanced Aviation Weather Radar Data Processing and Real-Time Implementations

The objectives of this dissertation work are developing an enhanced intelligent radar signal and data processing framework for aviation hazard detection, classification and monitoring, and real-time implementation on massive parallel platforms. Variety of radar sensor platforms are used to prove the concept including airborne precipitation radar and different ground weather radars. As a focused example of the proposed approach, this research applies evolutionary machine learning technology to turbulence level classification for civil aviation. An artificial neural network (ANN) machine learning approach based on radar observation is developed for classifying the cubed root of the Eddy Dissipation Rate (EDR), a widely-accepted measure of turbulence intensity. The approach is validated using typhoon weather data collected by Hong Kong Observatory’s (HKO) Terminal Doppler Weather Radar (TDWR) located near Hong Kong International Airport (HKIA) and comparing HKO-TDWR EDR$^{1/3}$ detections and predictions with in situ EDR$^{1/3}$ measured by commercial aircrafts. The testing results verified that machine learning approach performs reasonably well for both detecting and predicting tasks. As the preliminary step to explore the possibility of acceleration by integrating General Purpose Graphic Processing Unit (GPGPU), this research introduces a practical approach to implement real-time processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar. After the investigation of the GPGPU on radar signal processing chain, the benchmark of applying machine learning approach on embedded GPU platform was performed. According to the performance, real-time requirement of the machine learning method of turbulence detection developed in this research could be met as well as Size, Weight and Power (SWaP) restrictions on embedded GPGPU platforms

GPU devices for safety-critical systems: a survey

Graphics Processing Unit (GPU) devices and their associated software programming languages and frameworks can deliver the computing performance required to facilitate the development of next-generation high-performance safety-critical systems such as autonomous driving systems. However, the integration of complex, parallel, and computationally demanding software functions with different safety-criticality levels on GPU devices with shared hardware resources contributes to several safety certification challenges. This survey categorizes and provides an overview of research contributions that address GPU devices’ random hardware failures, systematic failures, and independence of execution.This work has been partially supported by the European Research Council with Horizon 2020 (grant agreements No. 772773 and 871465), the Spanish Ministry of Science and Innovation under grant PID2019-107255GB, the HiPEAC Network of Excellence and the Basque Government under grant KK-2019-00035. The Spanish Ministry of Economy and Competitiveness has also partially supported Leonidas Kosmidis with a Juan de la Cierva Incorporación postdoctoral fellowship (FJCI-2020- 045931-I).Peer ReviewedPostprint (author's final draft