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Application Acceleration

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Katherine Compton – One of the best experts on this subject based on the ideXlab platform.

  • scientific Application Acceleration with reconfigurable functional units
    Field-Programmable Custom Computing Machines, 2007
    Co-Authors: Kyle Rupnow, Keith D Underwood, Katherine Compton

    Abstract:

    While scientific Applications in the past were limited by floating point computations, modern scientific Applications use more unstructured formulations. These Applications have a significant percentage of integer computation – increasingly a limiting factor in scientific Application performance. In real scientific Applications employed at Sandia National Labs, integer computations constitute on average 37% of the Application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze Application traces of Sandia’s scientific Applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the Acceleration potential of the Applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia Applications show an improvement of 5% or more.

  • FCCM – Scientific Application Acceleration with Reconfigurable Functional Units
    15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2007), 2007
    Co-Authors: Kyle Rupnow, Keith D Underwood, Katherine Compton

    Abstract:

    While scientific Applications in the past were limited by floating point computations, modern scientific Applications use more unstructured formulations. These Applications have a significant percentage of integer computation – increasingly a limiting factor in scientific Application performance. In real scientific Applications employed at Sandia National Labs, integer computations constitute on average 37% of the Application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze Application traces of Sandia’s scientific Applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the Acceleration potential of the Applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia Applications show an improvement of 5% or more.

Kyle Rupnow – One of the best experts on this subject based on the ideXlab platform.

  • scientific Application Acceleration with reconfigurable functional units
    Field-Programmable Custom Computing Machines, 2007
    Co-Authors: Kyle Rupnow, Keith D Underwood, Katherine Compton

    Abstract:

    While scientific Applications in the past were limited by floating point computations, modern scientific Applications use more unstructured formulations. These Applications have a significant percentage of integer computation – increasingly a limiting factor in scientific Application performance. In real scientific Applications employed at Sandia National Labs, integer computations constitute on average 37% of the Application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze Application traces of Sandia’s scientific Applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the Acceleration potential of the Applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia Applications show an improvement of 5% or more.

  • FCCM – Scientific Application Acceleration with Reconfigurable Functional Units
    15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2007), 2007
    Co-Authors: Kyle Rupnow, Keith D Underwood, Katherine Compton

    Abstract:

    While scientific Applications in the past were limited by floating point computations, modern scientific Applications use more unstructured formulations. These Applications have a significant percentage of integer computation – increasingly a limiting factor in scientific Application performance. In real scientific Applications employed at Sandia National Labs, integer computations constitute on average 37% of the Application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze Application traces of Sandia’s scientific Applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the Acceleration potential of the Applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia Applications show an improvement of 5% or more.

Keith D Underwood – One of the best experts on this subject based on the ideXlab platform.

  • scientific Application Acceleration with reconfigurable functional units
    Field-Programmable Custom Computing Machines, 2007
    Co-Authors: Kyle Rupnow, Keith D Underwood, Katherine Compton

    Abstract:

    While scientific Applications in the past were limited by floating point computations, modern scientific Applications use more unstructured formulations. These Applications have a significant percentage of integer computation – increasingly a limiting factor in scientific Application performance. In real scientific Applications employed at Sandia National Labs, integer computations constitute on average 37% of the Application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze Application traces of Sandia’s scientific Applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the Acceleration potential of the Applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia Applications show an improvement of 5% or more.

  • FCCM – Scientific Application Acceleration with Reconfigurable Functional Units
    15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2007), 2007
    Co-Authors: Kyle Rupnow, Keith D Underwood, Katherine Compton

    Abstract:

    While scientific Applications in the past were limited by floating point computations, modern scientific Applications use more unstructured formulations. These Applications have a significant percentage of integer computation – increasingly a limiting factor in scientific Application performance. In real scientific Applications employed at Sandia National Labs, integer computations constitute on average 37% of the Application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze Application traces of Sandia’s scientific Applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the Acceleration potential of the Applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia Applications show an improvement of 5% or more.