Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The StreamIt programming model has been proposed to exploit parallelism in streaming applications on general purpose multi-core architectures. This model allows programmers to specify the structure of a program as a set of filters that act upon data, and a set of communication channels beten them. The StreamIt graphs describe task, data and pipeline parallelism which can be exploited on modern Graphics Processing Units (GPUs), as they support abundant parallelism in hardware. ,  describe the challenges in mapping StreamIt to GPUs and propose an efficient technique to software pipeline the execution of stream programs on GPUs.  formulate this problem --- both scheduling and assignment of filters to processors --- as an efficient Integer Linear Program (ILP), which is then solved using ILP solvers.  also describe a novel buffer layout technique for GPUs which facilitates exploiting the high memory bandwidth available in GPUs. The proposed scheduling utilizes both the scalar units in GPU, to exploit data parallelism, and multiprocessors, to exploit task and pipeline parallelism.  Further it takes into consideration the synchronization and bandwidth limitations of GPUs, and yields speedups beten 1.87X and 36.83X over a single threaded CPU.
['Abhishek Udupa', 'Ramaswamy Govindarajan', 'Matthew J. Thazhuthaveetil']
I am writing a research paper on a idea Software Pipelined Execution of Stream Programs on GPUs
[{"from": "human", "value": "I am writing a research paper on a idea Software Pipelined Execution of Stream Programs on GPUs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The StreamIt programming model has been proposed to exploit parallelism in streaming applications on general purpose multi-core architectures. This model allows programmers to specify the structure of a program as a set of filters that act upon data, and a set of communication channels beten them. The StreamIt graphs describe task, data and pipeline parallelism which can be exploited on modern Graphics Processing Units (GPUs), as they support abundant parallelism in hardware. ,  describe the challenges in mapping StreamIt to GPUs and propose an efficient technique to software pipeline the execution of stream programs on GPUs.  formulate this problem --- both scheduling and assignment of filters to processors --- as an efficient Integer Linear Program (ILP), which is then solved using ILP solvers.  also describe a novel buffer layout technique for GPUs which facilitates exploiting the high memory bandwidth available in GPUs. The proposed scheduling utilizes both the scalar units in GPU, to exploit data parallelism, and multiprocessors, to exploit task and pipeline parallelism.\u00a0\u00a0Further it takes into consideration the synchronization and bandwidth limitations of GPUs, and yields speedups beten 1.87X and 36.83X over a single threaded CPU."}]