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.

Low-Density Parity-Check (LDPC) codes are powerful error correcting codes adopted by recent communication standards. LDPC decoders are based on belief propagation algorithms, which make use of a Tanner graph and very intensive message-passing computation, and usually require hardware-based dedicated solutions. With the exponential increase of the computational power of commodity graphics processing units (GPUs), new opportunities have arisen to develop general purpose processing on GPUs. This paper proposes the use of GPUs for implementing flexible and programmable LDPC decoders. A new stream-based approach is proposed, based on compact data structures to represent the Tanner graph. It is shown that such a challenging application for stream-based computing, because of irregular memory access patterns, memory bandwidth and recursive flow control constraints, can be efficiently implemented on GPUs. The proposal was experimentally evaluated by programming LDPC decoders on GPUs using the Caravela platform, a generic interface tool for managing the kernels' execution regardless of the GPU manufacturer and operating system. Moreover, to relatively assess the obtained results, we have also implemented LDPC decoders on general purpose processors with Streaming Single Instruction Multiple Data (SIMD) Extensions. Experimental results show that the solution proposed here efficiently decodes several codewords simultaneously, reducing the processing time by one order of magnitude.

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