A code motion technique for accelerating general-purpose computation on the GPU

Abstract. Thanks to the nature of the graphics processing, the newly released products offer highly parallel processing units with high-memory bandwidth and computational power of more than teraflops per second. The modern GPUs are not only powerful graphic engines but also they are high level parallel programmable processors with very fast computing capabilities and high-memory bandwidth speed compared to central processing units (CPU). Data-parallel computations can be shortly described as mapping data elements to parallel processing threads. The rapid development of GPUs programmability and capabilities attracted the attentions of researchers dealing with complex problems which need high level calculations. This interest has revealed the concepts of “General Purpose Computation on Graphics Processing Units (GPGPU)” and “stream processing”. The graphic processors are powerful hardware which is really cheap and affordable. So the graphic processors became an alternative to computer processors. The graphic chips which were standard application hardware have been transformed into modern, powerful and programmable processors to meet the overall needs. Especially in recent years, the phenomenon of the usage of graphics processing units in general purpose computation has led the researchers and developers to this point. The biggest problem is that the graphics processing units use different programming models unlike current programming methods. Therefore, an efficient GPU programming requires re-coding of the current program algorithm by considering the limitations and the structure of the graphics hardware. Currently, multi-core processors can not be programmed by using traditional programming methods. Event procedure programming method can not be used for programming the multi-core processors. GPUs are especially effective in finding solution for repetition of the computing steps for many data elements when high accuracy is needed. Thus, it provides the computing process more quickly and accurately. Compared to the GPUs, CPUs which perform just one computing in a time according to the flow control are slower in performance. This structure can be evaluated for various applications of computer technology. In this study covers how general purpose parallel programming and computational power of the GPUs can be used in photogrammetric applications especially direct georeferencing. The direct georeferencing algorithm is coded by using GPGPU method and CUDA (Compute Unified Device Architecture) programming language. Results provided by this method were compared with the traditional CPU programming. In the other application the projective rectification is coded by using GPGPU method and CUDA programming language. Sample images of various sizes, as compared to the results of the program were evaluated. GPGPU method can be used especially in repetition of same computations on highly dense data, thus finding the solution quickly.

Recently, general-purpose computation on graphics processing units (GPGPU) has become an increasingly popular field of study as graphics processing units (GPUs) continue to be proposed as high performance and relatively low cost implementation platforms for scientific computing applications. Among these applications figure astrophysical N-bodysimulations, which form one of the most challenging problems in computational science. However, in most reported studies, a simple $ \mathcal{O}(N^{2})$ algorithm was used for GPGPUs, and the resulting performances were not observed to be better than those of conventional CPUs that were based on more optimized $ \mathcal{O}(N \log N)$ algorithms such as the tree algorithm or the particle-particle particle-mesh algorithm. Because of the difficulty in getting efficient implementations of such algorithms on GPUs, a GPU cluster had no practical advantage over general-purpose PC clusters for N-bodysimulations. In this paper, we report a new method for efficient parallel implementation of the tree algorithm on GPUs. Our novel tree code allows the realization of an N-bodysimulation on a GPU cluster at a much higher performance than that on general PC clusters. We practically performed a cosmological simulation with 562 million particles on a GPU cluster using 128 NVIDIA GeForce 8800GTS GPUs at an overall cost of 168172 $. We obtained a sustained performance of 20.1 Tflops, which when normalized against a general-purpose CPU implementation leads to a performance of 8.50 Tflops. The achieved cost/performance was hence a mere $19.8 /Gflops which shows the high competitiveness of GPGPUs.

Graphics processing units (GPUs) 1 have enjoyed increasing popularity in recent years, which benefits from, for example, general-purpose GPU (GPGPU) for parallel programs and new computing paradigms, such as the Internet of Things (IoT). GPUs hold great potential in providing effective solutions for big data analytics while the demands for processing large quantities of data in real time are also increasing. However, the pervasive presence of GPUs on mobile devices presents great challenges for GPGPU, mainly because GPGPU integrates a large amount of processor arrays and concurrent executing threads (up to hundreds of thousands). In particular, the root causes of performance loss in a GPGPU program can not be revealed in detail by current approaches. In this article, we propose MiC (Multi-level Characterization), a framework that comprehensively characterizes GPGPU kernels at the instruction, Basic Block (BBL), and thread levels. Specifically, we devise Instruction Vectors (IV) and Basic Blocks Vectors (BBV), a Thread Similarity Matrix (TSM), and a Divergence Flow Statistics Graph (DFSG) to profile information in each level. We use MiC to provide insights into GPGPU kernels through the characterizations of 34 kernels from popular GPGPU benchmark suites such as Compute Unified Device Architecture (CUDA) Software Development Kit (SDK), Rodinia, and Parboil. In comparison with Central Processing Unit (CPU) workloads, we conclude the key findings as follows: (1) There are comparable Instruction-Level Parallelism (ILP); (2) The BBL count is significantly smaller than CPU workloads—only 22.8 on average; (3) The dynamic instruction count per thread varies from dozens to tens of thousands and it is extremely small compared to CPU benchmarks; (4) The Pareto principle (also called 90/10 rule) does not apply to GPGPU kernels while it pervasively exists in CPU programs; (5) The loop patterns are dramatically different from those in CPU workloads; (6) The branch ratio is lower than that of CPU programs but higher than pure GPU workloads. In addition, we have also shown how TSM and DFSG are used to characterize the branch divergence in a visual way, to enable the analysis of thread behavior in GPGPU programs. In addition, we show an optimization case for a GPGPU kernel from the bottleneck identified through its characterization result, which improves 16.8% performance.

Nowadays graphics processing units (GPUs) have become popular parallel computing platforms known as General-Purpose GPU (GPGPU) computing. GPUs thereby are chosen by some security researchers as cryptographic accelerators to secure massive volumes of transactions. However, their security issues are ignored in spite of their popularity and performance. There are some possible information leakages faced with malicious attacks or even in the normal GPU computing. Our objective is to secure the confidentiality of cryptographic keys in GPU computing environments and provide easy-to-use programming with few constraints. In this paper, we propose a prototype in Linux, a system of GPGPU computing solution empowered by GPU virtualization technology, which keeps encrypted keys in guest machine to protect secret keys from leakage even in the event of full system compromise. With the API interception and redirection of CUDA, applications in Virtual Machines (VMs) can access the GPU device in a transparent way. Besides, we use virtio, a dedicated virtual I/O device, to transfer data between virtual and host machines in high performance. In our current study, we evaluate our prototype with the GPU implementation of ECC. We show that it can protect private keys of GPU cryptographic computation and it also incurs low performance penalty compared with the native environment, therefore demonstrating the prototype is secure and requires reasonable overhead.

Recently, GPGPU (General Purpose computation on Graphics Processing Units) has become popular with great success, especially in scientific fields such as fluid dynamics, image processing, and visualization using particle methods. In this paper, we discuss how the GPGPU is used in implementations of parallel ant colony optimization (ACO) for fast solution of quadratic assignment problems (QAPs). As for the ACO, we use the cunning ant system (cAS) which is one of the most promising ACO algorithms.

Fractal image coding is one of the most prominent compression technologies. It can be also used for industrial applications like image retrieval methods and image indexing methods. In addition, the adaptive approach can achieve the image compression with given quality level by changing the size of range blocks in any location, so the adaptive approach can achieve a high compression rate with less data than the non-adaptive method, but the naive parallel implementation may result in the inefficient parallelization due to the imbalance of work loads. On the other hand, GPGPU (General Purpose computing on Graphics Processing Unit) attracts a great deal of attention, which is used for general-purpose computations like numerical calculations as well as graphics processing. In this paper, we evaluate three parallel programs for the adaptive fractal image coding algorithm on GPUs by using CUDA (Compute Unified Device Architecture) on Nvidia GTX Titan GPU and discuss the effectiveness of parallel programs using index vectors and multithreading. Especially, the third program may enhance the occupancy of GPU computing by distributing the reduction computing over several processing cores.

Image processing has high computational requirements. As many papers (including [Frisk 2010; N. Zhang and Wang 2010; Yang et al. 2008]) have indicated, a number of image processing operations can be optimized by heavy parallelization of the computation. Currently, one of the best options for parallelized image processing is using general purpose computation on graphics processing units (GPGPU).In the area of GPGPU, a rather wide range of APIs is available and finding an appropriate choice for a project is challenging. In the course of being able to provide guidance on selecting the right API in an image processing context, four GPU programming models were compared according to their platform independence, usability, and performance. The four investigated APIs are CUDA, OpenCL 1.2, Vulkan, and SYCL.To gain information on the metrics usability and performance a test project was created, implementing a number of image processing tasks using the four GPU programming models. The test project was designed as a library, where the 4 investigated APIs could be replaced at compile-time and new operations could be easily added. The implemented reference algorithms form a pipeline for processing images from polarization cameras. The targeted platforms were mid- to high-end desktop PCs as well as embedded single board platforms ranging from Odroid to Nvidia Jetson. On all devices, either MSVC 19.x or GCC 7+ were used as host compilers. CUDA tests were compiled with NVCC and SYCL was represented by ComputeCpp and hipSYCL depending on the device.The direct comparison of the APIs, done for this work, cumulates into a decision matrix to aid organizations in their process of selecting an offloading API. It shows, for example, that CUDA and SYCL are comparable in terms of development cost, support of modern C++ and ease of integration in existing code bases. When considering modern standard adoption and GPU portability Vulkan has advantages, while OpenCL and SYCL can run on CPUs and FPGAs. In the tooling category, CUDA has the most specialized and effective tools whereas Vulkan has almost no compute specific tools available. CUDA and Vulkan provide the best performance (on the tested devices), while Vulkan was able to outperform CUDA on a Nvidia Jetson.It becomes clear that all four APIs have their advantages and may be suitable for different applications. Vulkan is the API of choice in mobile consumer applications. CUDA is convenient for fast development but only if it is feasible to support Nvidia GPUs only. For AMD GPU support the CUDA-like HIP can be used, although lacking Windows support and the tool quality on the AMD side. OpenCL might be chosen if a considerable number of different desktop GPUs and FPGAs should be supported. Although some metrics still show room for improvement, SYCL seems to grow into a good all-rounder with all the available implementations. It could benefit from weakening its tight integration with OpenCL to improve support on some platforms. A Vulkan implementation, for example, would open opportunities to target mobile and Apple devices, while hipSYCL could gain the status of a standard-conforming implementation.While these results provide a general and first overview of the differences, the pros, and cons of those APIs, it would be interesting to receive feedback from the Khronos developer community on their experiences and build more mature metrics on it.

Two benchmark tests were carried out to analyze the performance of OpenFOAM-based CFD solvers using General-Purpose computing on Graphics Processing Units(GPGPU). In the present study, RapidCFD, which is an implementation of OpenFOAM capable of running most of the functions of OpenFOAM on GPUs, was used to apply GPGPU to OpenFOAM. The numerical simulations of 1) 3D lid-driven cavity incompressible flows and 2) steady flows around a motorbike were conducted on two kinds of CPU, single-GPU, and multi-GPU systems, and the computational times were analyzed. For the test of cavity flows, as the number of cells increased, the performance and the scalability of GPGPU were improved. When the number of cells was 2503, a system with 8-GPUs showed the highest performance with 42 times of speedup over a CPU system. For the test of flows around a motorbike, a system with 8-GPUs showed the highest performance with 20 times of speedup over a CPU system. For both single precision and double precision calculations, the performance improvements using GPGPU were efficient. The results demonstrate that GPGPU would be more efficient than computing on CPUs when computing large-scale flows and practical problems that require massive parallelism.

Computational Fluid Dynamics (CFD) is widely used in industry and academic research to investigate complex fluid flow. The bottleneck of a realistic CFD simulation is its long simulation time. The simulation time is generally reduced by massively parallel Central Processing Unit (CPU) clusters, which are very expensive. In this paper, it is shown that the CFD simulation can be accelerated significantly by a novel hardware called General Purpose Computing on Graphical Processing Units (GPGPU). GPGPU is a cost-effective computing cluster, which uses the Compute Unified Device Architecture (CUDA) of NVIDIA devices to transform the GPU into a massively parallel processor. The paper demonstrates the faster computing ability of GPU compared to a traditional multi-core CPU. Two scenarios are simulated; one is a 2-dimensional simulation of regular wave and another one is a 3-dimensional motion of a floating ship on a regular wave. A smoothed particle hydrodynamics (SPH) based CFD solver is used for simulating the complex free-surface flow. The performance of a single GPU is compared against a commonly used 16 core CPU. For a large simulation of 6 degrees of freedom (DOF) ship motion simulation, the comparative study exhibits a speedup of more than an order of magnitude, reducing simulation time from 30 hours to about 2 hours. This indicates a CUDA enabled GPU card can be used as a cost-effective computing tool for a reliable and accurate SPH-based CFD simulation. The cost-benefit analysis of GPU over a CPU cluster is also discussed.

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.

The popularity of general purpose Graphic Processing Unit (GPU) is largely attributed to the tremendous concurrency enabled by its underlying architecture -- single instruction multiple thread (SIMT) architecture. It keeps the context of a significant number of threads in registers to enable fast ``context switches" when the processor is stalled due to execution dependence, memory requests and etc. The SIMT architecture has a large register file evenly partitioned among all concurrent threads. Per-thread register usage determines the number of concurrent threads, which strongly affects the whole program performance. Existing register allocation techniques, extensively studied in the past several decades, are oblivious to the register contention due to the concurrent execution of many threads. They are prone to making optimization decisions that benefit single thread but degrade the whole application performance.

The increasing adoption of graphics processing units (GPUs) for high-performance computing raises the reliability challenge, which is generally ignored in traditional GPUs. GPUs usually support thousands of parallel threads and require a sizable register file. Such large register file is highly susceptible to soft errors and power-hungry. Although ECC has been adopted to register file in modern GPUs, it causes considerable power overhead, which further increases the power stress. Thus, an energy-efficient soft-error protection mechanism is more desirable. Besides its extremely low leakage power consumption, resistive memory (e.g., spin-transfer torque RAM) is also immune to the radiation induced soft errors due to its magnetic field based storage. In this article, we propose to LEverage reSistive memory to enhance the Soft-error robustness and reduce the power consumption (LESS) of registers in the General-Purpose computing on GPUs (GPGPUs). Since resistive memory experiences longer write latency compared to SRAM, we explore the unique characteristics of GPGPU applications to obtain the win-win gains: achieving the near-full soft-error protection for the register file, and meanwhile substantially reducing the energy consumption with negligible performance degradation. Our experimental results show that LESS is able to mitigate the registers soft-error vulnerability by 86% and achieve 61% energy savings with negligible (e.g., 1%) performance degradation.

This paper studies implementation methods of parallel text matching using General Purpose computing on Graphics Processing Unit (GPGPU). It is necessary to accelerate text matching in applications of real-time processing, such as anomaly-detection and decision-making. GPGPU is a technology that can be used to accelerate a variety of applications with highly parallel processing elements in GPUs. Recently, the Parallel-Failure-less Aho-Corasick (PFAC) algorithm has been developed, and an open-source PFAC library is currently available. However, there are several different implementation methods in the host-side, and choosing a good combination of these methods is important to improve the performance. We implemented a prototype system, and evaluated the performance and power consumption varying the implementation methods and input data. The evaluation results show that the performance of the system using GPGPU is better than a system using 4-core CPU with smaller power consumption.

For the past 40 years, Moore’s law has predicted the rapid growth of the computer industry. In the past few years, however, this growth has slowed for central processing units (CPUs). Instead, there has been a shift to multicore computing, specifically with the general purpose graphic processing units (GPUs). Conventional CPUs have between two and eight cores, but the GPUs can have hundreds, even thousands of cores. By parallelizing code, the computing power of these cores can be utilized to greatly accelerate the performance of certain algorithms. The GPUs, however, have been known to consume more power than the conventional processing units. While previous research has focused on the impact that the GPUs have on performance, there are much fewer studies on the impact of GPUs on energy consumption and efficiency. Some researchers have hypothesized that if the performance of an algorithm was sufficiently increased on a GPU, then the accelerated time would actually cause the GPU to consume less energy. For the first time to our knowledge, we study the energy efficiency of a GPU with an application to iris recognition. Using GPU-based code written in the C++ compatible compute unified device architecture language, energy consumption tests are performed on basic image processing techniques, including image inversion, thresholding, dilation, erosion, and memory/computationally intensive calculations, such as the template matching. We demonstrate that the portions of these algorithms implemented on the GPU reduce energy consumption by as much as 272 times.

Computer models of water distribution networks are commonly used to simulate large systems under complex dynamic scenarios. These models normally use so-called demand-driven solvers, which determine the nodal pressures and pipe flow rates that correspond to specified nodal demands. This paper investigates the use of data parallel high performance computing (HPC) techniques to accelerate demand-driven hydraulic solvers. The sequential code of the solver implemented in the CWSNet library is analysed to understand which computational blocks contribute the most to the total computation time of a hydraulic simulation. The results obtained show that, contrary to popular belief, the linear solver is not the code block with the highest impact on the simulation time, but the pipe head loss computation. Two data parallel HPC techniques, single instruction multiple data (SIMD) operations and general purpose computation on graphics processing units (GPGPU), are used to accelerate the pipe head loss computation and linear algebra operations in new implementations of the hydraulic solver of CWSNet library. The results obtained on different network models show that the use of this techniques can improve significantly the performance of a demand-driven hydraulic solver.