University of Cambridge > > Computer Laboratory Programming Research Group Seminar > Performance-portable Programming Abstraction for Image Processing

Performance-portable Programming Abstraction for Image Processing

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We present a framework for representing image processing kernels based on decoupled access/execute metadata, which allow the programmer to specify both execution constraints and memory access pattern of a kernel. The framework performs source-to-source translation of kernels expressed in high-level framework-specific C++ classes into low-level CUDA or OpenCL code with effective device-dependent optimizations such as global memory padding for avoiding partition conflicts. We evaluate the framework on two kernels on GPU and CPU systems, and concur with previous work that access/execute metadata is a suitable performance-portable abstraction for image processing.

This talk is part of the Computer Laboratory Programming Research Group Seminar series.

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