BSGP: bulk-synchronous GPU programming (original) (raw)

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...

General purpose computing on graphics processing units (GPGPU) consists of using GPUs to handle computations commonly handled by CPUs. GPGPU programming implies developing specific programs to run on GPUs managed by a host program running on the CPU. To achieve high performance implies to explicitly organize memory transfers between devices. Besides, different incompatible frameworks exist making productivity and portability difficult to achieve.

CUDA is now the dominant language used for programming GPUs, one of the most exciting hardware developments of recent decades. With CUDA, you can use a desktop PC for work that would have previously required a large cluster of PCs or access to a HPC facility. As a result, CUDA is increasingly important in scientific and technical computing across the whole STEM community, from medical physics and financial modelling to big data applications and beyond. This unique book on CUDA draws on the author's passion for and long experience of developing and using computers to acquire and analyse scientific data. The result is an innovative text featuring a much richer set of examples than found in any other comparable book on GPU computing. Much attention has been paid to the C++ coding style, which is compact, elegant and efficient. A code base of examples and supporting material is available online, which readers can build on for their own projects.

As a consequence of the immense computational power avail-able in GPUs, the usage of these platforms for running data-intensive general purpose programs has been increasing. Since memory and pro-cessor architectures of CPUs and GPUs are substantially different, pro-grams designed for each platform are also very different and often resort to a very distinct set of algorithms and data structures. Selecting be-tween the CPU or GPU for a given program is not easy as there are variations in the hardware of the GPU, in the amount of data, and in several other performance factors. AEminiumGPU is a new data-parallel framework for developing and run-ning parallel programs on CPUs and GPUs. AEminiumGPU programs are written in a Java using Map-Reduce primitives and are compiled into hybrid executables which can run in either platforms. Thus, the de-cision of which platform is going to be used for executing a program is delayed until run-time and automatically performed by the system using Mach...

Abstract Graphical Processing Unit (GPU) programming languages are used extensively for general-purpose computations. However, GPU programming languages are at a level of abstraction suitable only for use by expert parallel programmers. This paper presents a new approach through whichC'or Java programmers can access these languages without having to focus on the technical or language-specific details.