GP-GPU Accelerated Supercomputing

2013

In this paper we study NVIDIA graphics processing unit (GPU) along with its computational power and applications. Although these units are specially designed for graphics application we can employee there computation power for non graphics application too. GPU has high parallel processing power, low cost of computation and less time utilization; it gives good result of performance per energy ratio. This GPU deployment property for excessive computation of similar small set of instruction played a significant role in reducing CPU overhead. GPU has several key advantages over CPU architecture as it provides high parallelism, intensive computation and significantly higher throughput. It consists of thousands of hardware threads that execute programs in a SIMD fashion hence GPU can be an alternate to CPU in high performance environment and in supercomputing environment. The base line is GPU based general purpose computing is a hot topics of research and there is great to explore rather ...

2009

— In this paper we describe GPU and its computing. GPU (Graphics Processing Unit) is an extremely multi-threaded architecture and then is broadly used for graphical and now non-graphical computations. The main advantage of GPUs is their capability to perform significantly more floating point operations (FLOPs) per unit time than a CPU. GPU computing increases hardware capabilities and improves programmability. By giving a good price or performance benefit, core-GPU can be used as the best alternative and complementary solution to multi-core servers. In fact, to perform network coding simultaneously, multi core CPUs and many-core GPUs can be used. It is also used in media streaming servers where hundreds of peers are served concurrently. GPU computing is the use of a GPU (graphics processing unit) together with a CPU to accelerate general-purpose scientific and engineering applications. GPU was first manufactured by NVIDIA. CPUs have few cores which is used for serial processing and GPUs have thousands of smaller cores which are more efficient, designed for parallel processing. So, CPU + GPU is a powerful combination. Whenever the code is run on the machine, CPU runs serial portion and GPU runs parallel portion. GPU is used for general purpose applications like arithmetic and it is also used for gaming.

Computer, 2000

Pollack Periodica, 2008

The evolution of GPUs (graphics processing units) has been enormous in the past few years. Their calculation power has improved exponentially, while the range of the tasks computable on GPUs has got significantly wider. The milestone of GPU development of the recent years is the appearance of the unified architecture-based devices. These GPUs implement a massively parallel design, which led them be capable not only of processing the common computer graphics tasks, but qualifies them for performing highly parallel mathematical algorithms effectively. Recognizing this availability GPU providers have issued developer platforms, which let the programmers manage computations on the GPU as a data-parallel computing device without the need of mapping them to a graphics API. Researchers salute this initiative, and the application of the new technology is quickly spreading in various branches of science

Queue, 2008

A gamer wanders through a virtual world rendered in near- cinematic detail. Seconds later, the screen fills with a 3D explosion, the result of unseen enemies hiding in physically accurate shadows. Disappointed, the user exits the game and returns to a computer desktop that exhibits the stylish 3D look-and-feel of a modern window manager. Both of these visual experiences require hundreds of gigaflops of computing performance, a demand met by the GPU (graphics processing unit) present in every consumer PC.

ArXiv, 2014

Since the first idea of using GPU to general purpose computing, things have evolved over the years and now there are several approaches to GPU programming. GPU computing practically began with the introduction of CUDA (Compute Unified Device Architecture) by NVIDIA and Stream by AMD. These are APIs designed by the GPU vendors to be used together with the hardware that they provide. A new emerging standard, OpenCL (Open Computing Language) tries to unify different GPU general computing API implementations and provides a framework for writing programs executed across heterogeneous platforms consisting of both CPUs and GPUs. OpenCL provides parallel computing using task-based and data-based parallelism. In this paper we will focus on the CUDA parallel computing architecture and programming model introduced by NVIDIA. We will present the benefits of the CUDA programming model. We will also compare the two main approaches, CUDA and AMD APP (STREAM) and the new framwork, OpenCL that tries...

2011

is head of the Microelectronics Research Group at the University of Bristol and chair of the Many-Core and Reconfigurable Supercomputing Conference (MRSC), Europe's largest conference dedicated to the use of massively parallel computer architectures. Prior to joining the university he spent fifteen years in industry where he designed massively parallel hardware and software at companies such as Inmos, STMicroelectronics and Pixelfusion, before co-founding ClearSpeed as Vice-President of Architecture and Applications. In 2006 ClearSpeed's many-core processors enabled the creation of one of the fastest and most energy-efficient supercomputers: TSUBAME at Tokyo Tech. He has given many invited talks on how massively parallel, heterogeneous processors are revolutionising high-performance computing, and has published numerous papers on how to exploit the speed of graphics processors for scientific applications. He holds eight patents in parallel hardware and software and sits on the steering and programme committees of most of the well-known international high-performance computing conferences.

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Journal of Computer Science and Technology, 2012