the latest research in mapping general-purpose computation to graphics hardware. We begin with the technical motivations that underlie general-purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. We then aim

or more networks. The participating processors may be scalar machines, multiprocessors, or special-purpose computers, enabling application components to execute on the architecture most appropriate to the algorithm. PVM provides a straightforward and general interface that permits the description

. At the same time, a number of microprocessor architectures have emerged which have VLIW and SIMD structures that are well matched to the needs of the ILP compilers. Most of these processors are targeted at embedded applications such as multimedia and communications, rather than general-purpose systems

the necessary peak computation power in general-purpose systems. However, for a large class of applications in embedded real-time systems like cellular phones and camcorders, the variable operating frequency interferes with their deadline guarantee mechanisms, and DVS in this context, despite its growing

Typical reconfigurable machines exhibit shortcomings that make them less than ideal for general-purpose computing. The Garp Architecture combines reconfigurable hardware with a standard MIPS processor on the same die to retain the better features of both. Novel aspects of the architecture

Abstract — A GPU-style processor has large amount of processing power on a given die compared to a general purpose processor. However, a Graphic Processing Unit must be executed in lock-step where a group of cores execute the same instruction. This constraint puts a real limitation on programming

Abstract—This work proposed a design of a processor that unifies the execution of Graphic Processing Units and a general purpose processor. This design is evolved from a simple Graphic Processing softcore where all cores execute the same instruction. The discussion of programming model

Abstract-In this paper we investigate possible ways to improve the energy efficiency of a general purpose microprocessor. We show that the energy of a processor depends on its performance, so we chose the energy-delay product to compare different processors. To improve the energy-delay product we

By taking advantage of the streaming processing model, modern graphics processors (GPUs) are outperforming their CPU counterparts in some general-purpose applications, and the difference is expected to grow in the future [7]. In this paper, we describe novel techniques for transforming irregular

(version 10.3) on a 12 core Intel Xeon X5670 machine. In addition, we show that the same data distribution is also suitable for general purpose GPU processors, where our implementation obtains up to 90 GFlops on a NVIDIA GeForce GTX480. This is about 2 times faster than the QRP implementation