GStream: A General Purpose Data-Streaming Framework on GPU Clusters Project summary Emerging accelerating architectures, such as GPUs, have proved successful in providing significant performance gains to various application domains. However, their viability to operate on general streaming data is still unknown. We propose GStream, a general-purpose, scalable data-streaming framework on GPUs. The objectives of GStream are as follows: (1) To provide powerful, yet concise language abstractions suitable to describe conventional algorithms as streaming problems. (2) To project these abstractions onto GPUs to fully exploit their inherent massive data-parallelism. (3) To show the viability of streaming on accelerators in experiments to assess flexibility, programmability and performance gains for various benchmarks from a variety of domains, including but not limited to data streaming, data parallel problems, numerical codes and text search. The proposed work will benefit stream-based and high-end data-intensive computing for GPUs, specifically in the area of massively data-parallel processing to support scalability, and to adapt to changing environments. Participants: Frank Mueller Yongpeng Zhang Publications: "GStream: A General-Purpose Data Streaming Framework on GPUs" by Yongpeng Zhang, Frank Mueller in GPU Technology Conference, Sep 2010 (talk and poster). "Large-Scale Multi-Dimensional Document Clustering on GPU Clusters" by Y. Zhang, F. Mueller, X. Cui and T. Potok in Journal of Parallel and Distributed Computing, V ??, No ?, Aug 2010 (accepted), pages ???-???. "Large-Scale Multi-Dimensional Document Clustering on GPU Clusters" by Y. Zhang, F. Mueller, X. Cui and T. Potok in International Parallel and Distributed Processing Symposium, Apr 2010. "A Programming Model for Massive Data Parallelism with Data Dependencies" by Y. Zhang, F. Mueller, X. Cui and T. Potok in Workshop on Programming Models for Emerging Architectures, Sep 2009. "GPU-Accelerated Text Mining" by Y. Zhang, F. Mueller, X. Cui and T. Potok in Workshop on Exploiting Parallelism using GPUs and other Hardware-Assisted Methods, Mar 2009.