Compute Unified Device Architecture Programming Model Research Articles

Structural testing for CUDA programming model

SummaryGraphic processors offer an accessible solution for high‐performance computing, addressing challenges across various fields. The Compute Unified Device Architecture (CUDA) programming model has emerged to enhance the performance of general‐purpose applications on graphic processors. However, developing CUDA programs is far from straightforward, and developers' lack of experience in parallel programming has led to numerous issues. This article presents a structural testing model and criteria to improve the quality of CUDA programs. These criteria facilitate the selection of test cases and aid in identifying faults. The ValiCUDA tool was developed to implement and validate this testing model and criteria. This tool instruments and analyzes programs, generating the necessary elements for each testing criterion. It also facilitates program execution and evaluation of criterion coverage. A statistical validation experiment assessed these criteria' effectiveness, cost, and strength metrics. The results demonstrate that the criteria can identify nontrivial faults in CUDA programs and assist testers in their testing endeavors for such applications.

RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization.

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

Survey of using GPU CUDA programming model in medical image analysis

Abstract With the technology development of medical industry, processing data is expanding rapidly and computation time also increases due to many factors like 3D, 4D treatment planning, the increasing sophistication of MRI pulse sequences and the growing complexity of algorithms. Graphics processing unit (GPU) addresses these problems and gives the solutions for using their features such as, high computation throughput, high memory bandwidth, support for floating-point arithmetic and low cost. Compute unified device architecture (CUDA) is a popular GPU programming model introduced by NVIDIA for parallel computing. This review paper briefly discusses the need of GPU CUDA computing in the medical image analysis. The GPU performances of existing algorithms are analyzed and the computational gain is discussed. A few open issues, hardware configurations and optimization principles of existing methods are discussed. This survey concludes the few optimization techniques with the medical imaging algorithms on GPU. Finally, limitation and future scope of GPU programming are discussed.

GPU-accelerated finite-difference time-domain method for dielectric media based on CUDA

The simulation of electromagnetic (EM) waves propagation in the dielectric media is presented using Compute Unified Device Architecture (CUDA) implementation of finite-difference time-domain (FDTD) method on graphic processing unit (GPU). The FDTD formulation in the dielectric media is derived in detail, and GPU-accelerated FDTD method based on CUDA programming model is described in the flowchart. The accuracy and speedup of the presented CUDA-implemented FDTD method are validated by the numerical simulation of the EM waves propagating into the lossless and lossy dielectric media from the free space on GPU, by comparison with the original FDTD method on CPU. The comparison of the numerical results of CUDA-implemented FDTD method on GPU and original FDTD method on CPU demonstrates that the CUDA-implemented FDTD method on GPU can obtain better application speedup performance with reasonable accuracy. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:512–518, 2016.

GPU acceleration of the WSM6 cloud microphysics scheme in GRAPES model

The microphysical process that leads to cloud and precipitation formation is one of the most important physical processes in numerical weather prediction (NWP) and climate models. The Weather Research Forecast (WRF) Single Moment 6-class (WSM6) microphysics scheme in the Global/Regional Assimilation and Prediction System (GRAPES) includes predictive variables of water vapor, cloud water, cloud ice, rain, snow and graupel. The computation of WSM6 is the most time-consuming portion among that of the entire GRAPES model. In recent years, with the advent of the Compute Unified Device Architecture (CUDA), modern graphics processing units (GPUs) with the advantage of low-power, low-cost, and high-performance computing capacity have been exploited to accomplish the arithmetic operations in scientific and engineering simulations. In this paper, we present an implementation of the WSM6 scheme in GRAPES using GPU to accelerate the computation. After a brief introduction to the WSM6 scheme, the data dependence for the GPU implementation of the WSM6 scheme is discussed. The data parallel method is employed to exploit the massive fine-grained parallelism. The CUDA programming model is used to convert the original WSM6 module into GPU programs. To achieve high computational performance, mapping horizontal domain onto an optimal block size is proposed. The experimental results demonstrate that the GPU version obtains over 140× speedup compared with the CPU serial version, and is an efficient parallel implementation.

Quantum computer simulation using the CUDA programming model

Quantum computing emerges as a field that captures a great theoretical interest. Its simulation represents a problem with high memory and computational requirements which makes advisable the use of parallel platforms. In this work we deal with the simulation of an ideal quantum computer on the Compute Unified Device Architecture (CUDA), as such a problem can benefit from the high computational capacities of Graphics Processing Units (GPU). After all, modern GPUs are becoming very powerful computational architectures which is causing a growing interest in their application for general purpose. CUDA provides an execution model oriented towards a more general exploitation of the GPU allowing to use it as a massively parallel SIMT (Single-Instruction Multiple-Thread) multiprocessor. A simulator that takes into account memory reference locality issues is proposed, showing that the challenge of achieving a high performance depends strongly on the explicit exploitation of memory hierarchy. Several strategies have been experimentally evaluated obtaining good performance results in comparison with conventional platforms.