Graphics Processing Units are emerging as a general-purpose high-performance computing devices (GPGPUs). Although this has led the creation of numerous GPGPU workloads available, there is a lack of a systematic approach to characterize GPGPU-applications. This paper proposes a similarity-based methodology for the characterization of GPU workloads. The proposed methodology successfully characterizes GPGPU workloads using kernel signatures and clustering algorithms. The signatures are derived from architecture-aware features of the workload, in particular from hardware performance counters. The evaluation of the proposed characterization approach includes a diversity of GPU benchmark suites such as Nvidia CUDA SDK, Parboil and Rodinia.


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    Title :

    Characterization of GPGPU Workloads via Correlation-Driven Kernel Similarity Analysis




    Publication date :

    2013-11-01


    Size :

    486872 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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