Advanced Topics GPU Programming and CUDA Architecture

High Performance ◽

Programming Model ◽

Direct Access ◽

Gpu Programming ◽

Processing Unit ◽

Computing Platform ◽

Cuda Architecture ◽

Graphics processing unit (GPU), which typically handles computation only for computer graphics. Any GPU providing a functionally complete set of operations performed on arbitrary bits can compute any computable value. Additionally, the use of multiple graphics cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing. CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs). CUDA gives program developers direct access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. This chapter first discuss some features and challenges of GPU programming and the effort to address some of the challenges with building and running GPU programming in high performance computing (HPC) environment. Finally this chapter point out the importance and standards of CUDA architecture.

Optimization of K-Means Clustering on Graphics Processing Unit Using Compute Unified Device Architecture

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2017.6274 ◽

2017 ◽

Vol 14 (1) ◽

pp. 789-795

Author(s):

V Saveetha ◽

S Sophia

Keyword(s):

High Performance ◽

Programming Model ◽

Direct Access ◽

Communication Overhead ◽

Processing Unit ◽

Compute Unified Device Architecture ◽

Central Processing ◽

Device Architecture ◽

Parallel data clustering aims at using algorithms and methods to extract knowledge from fat databases in rational time using high performance architectures. The computational challenge faced by cluster analysis due to increasing capacity of data can be overcome by exploiting the power of these architectures. The recent development in parallel power of Graphics Processing Unit enables low cost high performance solutions for general purpose applications. The Compute Unified Device Architecture programming model provides application programming interface methods to handle data proficiently on Graphics Processing Unit for iterative clustering algorithms like K-Means. The existing Graphics Processing Unit based K-Means algorithms highly focus on improvising the speedup of the algorithms and fall short to handle the high time spent on transfer of data between the Central Processing Unit and Graphics Processing Unit. A competent K-Means algorithm is proposed in this paper to lessen the transfer time by introducing a novel approach to check the convergence of the algorithm and utilize the pinned memory for direct access. This algorithm outperforms the other algorithms by maximizing parallelism and utilizing the memory features. The relative speedups and the validity measure for the proposed algorithm is elevated when compared with K-Means on Graphics Processing Unit and K-Means using Flag on Graphics Processing Unit. Thus the planned approach proves that communication overhead can be reduced in K-Means clustering.

A lightweight approach to performance portability with targetDP

The International Journal of High Performance Computing Applications ◽

10.1177/1094342016682071 ◽

2016 ◽

Vol 32 (2) ◽

pp. 288-301

Author(s):

Alan Gray ◽

Kevin Stratford

Keyword(s):

Particle Physics ◽

Message Passing ◽

High Performance ◽

Large Scale ◽

Message Passing Interface ◽

Processing Unit ◽

Performance Portability ◽

Leading high performance computing systems achieve their status through use of highly parallel devices such as NVIDIA graphics processing units or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus separate lattice quantum chromodynamics particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with Message Passing Interface (MPI) to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and graphics processing unit-accelerated large scale supercomputers.

Parallel SVD Algorithm for a Three-Diagonal Matrix on a Video Card Using the Nvidia CUDA Architecture

NaUKMA Research Papers Computer Science ◽

10.18523/2617-3808.2021.4.16-22 ◽

2021 ◽

Vol 4 ◽

pp. 16-22

Author(s):

Mykola Semylitko ◽

Gennadii Malaschonok

Keyword(s):

Computation Time ◽

Application Programming Interface ◽

General Purpose ◽

Diagonal Matrix ◽

Free Access ◽

Processing Unit ◽

Cuda Architecture ◽

SVD (Singular Value Decomposition) algorithm is used in recommendation systems, machine learning, image processing, and in various algorithms for working with matrices which can be very large and Big Data, so, given the peculiarities of this algorithm, it can be performed on a large number of computing threads that have only video cards.CUDA is a parallel computing platform and application programming interface model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit for general purpose processing – an approach termed GPGPU (general-purpose computing on graphics processing units). The GPU provides much higher instruction throughput and memory bandwidth than the CPU within a similar price and power envelope. Many applications leverage these higher capabilities to run faster on the GPU than on the CPU. Other computing devices, like FPGAs, are also very energy efficient, but they offer much less programming flexibility than GPUs.The developed modification uses the CUDA architecture, which is intended for a large number of simultaneous calculations, which allows to quickly process matrices of very large sizes. The algorithm of parallel SVD for a three-diagonal matrix based on the Givents rotation provides a high accuracy of calculations. Also the algorithm has a number of optimizations to work with memory and multiplication algorithms that can significantly reduce the computation time discarding empty iterations.This article proposes an approach that will reduce the computation time and, consequently, resources and costs. The developed algorithm can be used with the help of a simple and convenient API in C ++ and Java, as well as will be improved by using dynamic parallelism or parallelization of multiplication operations. Also the obtained results can be used by other developers for comparison, as all conditions of the research are described in detail, and the code is in free access.

A GPU-Based Wide-Band Radio Spectrometer

Publications of the Astronomical Society of Australia ◽

10.1017/pasa.2014.43 ◽

2014 ◽

Vol 31 ◽

Cited By ~ 11

Author(s):

Jayanth Chennamangalam ◽

Simon Scott ◽

Glenn Jones ◽

Hong Chen ◽

John Ford ◽

...

Keyword(s):

High Performance ◽

Wide Band ◽

Processing Unit ◽

Astronomical Instrumentation ◽

Online Data ◽

Polyphase Filter ◽

Polyphase Filter Bank ◽

AbstractThe graphics processing unit has become an integral part of astronomical instrumentation, enabling high-performance online data reduction and accelerated online signal processing. In this paper, we describe a wide-band reconfigurable spectrometer built using an off-the-shelf graphics processing unit card. This spectrometer, when configured as a polyphase filter bank, supports a dual-polarisation bandwidth of up to 1.1 GHz (or a single-polarisation bandwidth of up to 2.2 GHz) on the latest generation of graphics processing units. On the other hand, when configured as a direct fast Fourier transform, the spectrometer supports a dual-polarisation bandwidth of up to 1.4 GHz (or a single-polarisation bandwidth of up to 2.8 GHz).

A GPU/CPU Programming Model for CFD Simulation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.712-715.2538 ◽

2013 ◽

Vol 712-715 ◽

pp. 2538-2541

Author(s):

Cao Wei ◽

Zheng Hua Wang ◽

Chuan Fu Xu

Keyword(s):

High Performance ◽

Cfd Simulation ◽

Programming Model ◽

Processing Unit ◽

Computational Capability ◽

Parallel Programming Model ◽

Parallel Graphics ◽

Performance Results ◽

In recent years, the highly parallel graphics processing unit (GPU) is rapidly gaining maturity as a powerful engine for high performance computer. More and more researchers try to port the computational fluid dynamics (CFD) simulations into heterogeneous computers. However, most researchers focus on exploring the computational capability of GPU, while ignore the computational capability of CPU. In order to utilize the computational capability of CPU and GPU, we propose a hybrid CUDA/OpenMP parallel programming model. And we proposed an adaptive load balancing scheme to distribute the workload among CPUs and GPUs. With this programming model, we implement a high-order CFD program on “Tianhe-1A” supercomputer system. The performance results validate the workload distribution scheme.

Embedded GPU Implementation for High-Performance Ultrasound Imaging

Electronics ◽

10.3390/electronics10080884 ◽

2021 ◽

Vol 10 (8) ◽

pp. 884

Author(s):

Stefano Rossi ◽

Enrico Boni

Keyword(s):

High Performance ◽

Digital Signal ◽

Processing Unit ◽

Embedded Computing ◽

Field Programmable ◽

Peripheral Component Interconnect ◽

Programmable Gate Arrays ◽

Graphics Processing ◽

Signal Processors

Methods of increasing complexity are currently being proposed for ultrasound (US) echographic signal processing. Graphics Processing Unit (GPU) resources allowing massive exploitation of parallel computing are ideal candidates for these tasks. Many high-performance US instruments, including open scanners like ULA-OP 256, have an architecture based only on Field-Programmable Gate Arrays (FPGAs) and/or Digital Signal Processors (DSPs). This paper proposes the implementation of the embedded NVIDIA Jetson Xavier AGX module on board ULA-OP 256. The system architecture was revised to allow the introduction of a new Peripheral Component Interconnect Express (PCIe) communication channel, while maintaining backward compatibility with all other embedded computing resources already on board. Moreover, the Input/Output (I/O) peripherals of the module make the ultrasound system independent, freeing the user from the need to use an external controlling PC.

High-Performance, Graphics Processing Unit-Accelerated Fock Build Algorithm

Journal of Chemical Theory and Computation ◽

10.1021/acs.jctc.0c00768 ◽

2020 ◽

Vol 16 (12) ◽

pp. 7232-7238

Author(s):

Giuseppe M. J. Barca ◽

Jorge L. Galvez-Vallejo ◽

David L. Poole ◽

Alistair P. Rendell ◽

Mark S. Gordon

Keyword(s):

High Performance ◽

Processing Unit ◽

Ballooning Graphics Memory Space in Full GPU Virtualization Environments

Scientific Programming ◽

10.1155/2019/5240956 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11

Author(s):

Younghun Park ◽

Minwoo Gu ◽

Sungyong Park

Keyword(s):

High Performance ◽

Virtual Machines ◽

Performance Degradation ◽

Processing Unit ◽

Memory Space ◽

Memory Size ◽

Memory Sharing ◽

Gpu Virtualization ◽

Advances in virtualization technology have enabled multiple virtual machines (VMs) to share resources in a physical machine (PM). With the widespread use of graphics-intensive applications, such as two-dimensional (2D) or 3D rendering, many graphics processing unit (GPU) virtualization solutions have been proposed to provide high-performance GPU services in a virtualized environment. Although elasticity is one of the major benefits in this environment, the allocation of GPU memory is still static in the sense that after the GPU memory is allocated to a VM, it is not possible to change the memory size at runtime. This causes underutilization of GPU memory or performance degradation of a GPU application due to the lack of GPU memory when an application requires a large amount of GPU memory. In this paper, we propose a GPU memory ballooning solution called gBalloon that dynamically adjusts the GPU memory size at runtime according to the GPU memory requirement of each VM and the GPU memory sharing overhead. The gBalloon extends the GPU memory size of a VM by detecting performance degradation due to the lack of GPU memory. The gBalloon also reduces the GPU memory size when the overcommitted or underutilized GPU memory of a VM creates additional overhead for the GPU context switch or the CPU load due to GPU memory sharing among the VMs. We implemented the gBalloon by modifying the gVirt, a full GPU virtualization solution for Intel’s integrated GPUs. Benchmarking results show that the gBalloon dynamically adjusts the GPU memory size at runtime, which improves the performance by up to 8% against the gVirt with 384 MB of high global graphics memory and 32% against the gVirt with 1024 MB of high global graphics memory.