The q2 profiling framework: driving application mapping for heterogeneous reconfigurable platforms

  • Authors:

  • S. Arash Ostadzadeh;Roel Meeuws;Imran Ashraf;Carlo Galuzzi;Koen Bertels

  • Affiliations:

  • Computer Science and Engineering, Department of Software and Computer Technology, Delft University of Technology, Delft, The Netherlands;Computer Science and Engineering, Department of Software and Computer Technology, Delft University of Technology, Delft, The Netherlands;Computer Science and Engineering, Department of Software and Computer Technology, Delft University of Technology, Delft, The Netherlands;Computer Science and Engineering, Department of Software and Computer Technology, Delft University of Technology, Delft, The Netherlands;Computer Science and Engineering, Department of Software and Computer Technology, Delft University of Technology, Delft, The Netherlands

  • Venue:
  • ARC'12 Proceedings of the 8th international conference on Reconfigurable Computing: architectures, tools and applications
  • Year:
  • 2012

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Abstract

Heterogeneous multicore architectures pose specific challenges regarding their programmability and they require smart mapping schemes to make efficient use of different processing elements. Various criteria can drive this mapping, such as computational intensity, memory requirements, and area consumption. In order to facilitate this complex mapping task, there is a clear need for tools that investigate the use of such critical resources, like memory and hardware area. For this purpose, we developed the Q2profiling framework. It consists of two main parts: an advanced memory access profiling toolset, which provides detailed information on the runtime memory access patterns of an application and a statistical modeling component, which makes hardware area predictions early in the design phase based on software metrics. These tools are integrated using a partitioning methodology. We demonstrate the effectiveness of our framework using three applications in our experiments. One application is further detailed in a case study to illustrate the use of our methodology. Experimental results show application speedup of up to 2.92×.