Design and implementation of a stream-based distributedcomputing platform using graphics processing units

Abstract

Anonymous use of computing resources spread over the world becomes one of the main goals in GRID environments. In GRID-based computing, the security of users or of contributors of computing resources is crucial to execute processes in a safe way. This paper proposes a new method for streambased processing in a distributed environment and also a novel method to solve the security matter under this kind of processing. It also presents the design of the distributed computing platform developed for stream-based processing, including the description of the local and remote execution methods, which are collectively designated by Caravela platform. The proposed flow-model is mapped on the distributed processing resources, connected through a network, by using the Caravela platform. This platform has been developed by the authors of this paper specifically for making use of the Graphics Processing Units available in recent personal computers. The paper also illustrates application of the Caravela platform to different types of processing, namely scientific computing and image/video processing. The presented experimental results show that significant improvements can be achieved with the use of GPUs against the use of general purpose processors.

A Uniform Platform to Support Multigenerational GPUs for High Performance Stream-based Computing

“…Caravela platform [3,13,15,16] is an interface for stream-based computing implemented by the authors of this paper. The Caravela platform uses the concept of flow-model as shown in Figure 3 for programming a given stream-based computing task.…”

“…Providing a compiler-oriented programming interface based on kernel function framework, the Brook [1] was developed to absorb the difficulty to plan the calculation displaying pixels as the result. Caravela platform [3,13,15,16] was developed to form the true stream-based computing based on the flow-model that maintains the number of I/O streams, constant values and a program invoked on a targeted GPU. The recent GPU architecture makes available to provide the Brook style programming interface for GPGPU applications using CUDA or OpenCL.…”

A middleware for efficient stream processing in CUDA

“…However, some development frameworks focus on GPU-accelerated stream processing. For example, Yamagiwa et al [8] propose a stream-based distributed computing environment. Their environment is implemented using the DirectX graphics library.…”

Many-Task Computing on Many-Core Architectures

“…In the last years, the use of scheduler based on many-core or heterogeneous architectures for general or for specific applications has been widely studied [48,27]. S. Yamagiwa et al [48] propose a GPGPU streaming based on distributed computing environment; S. Nakagawa et al [27] provide a new middleware capable of out-of-order execution of works and data transfers using stream processing.…”

“…S. Yamagiwa et al [48] propose a GPGPU streaming based on distributed computing environment; S. Nakagawa et al [27] provide a new middleware capable of out-of-order execution of works and data transfers using stream processing. Other works [13,46] follow a similar strategy based on streaming to minimize data transfers overhead.…”