Hardware and compiler techniques for mapping data-parallel programs with divergent control flow to SIMD architectures have recently enabled the emergence of new GPGPU programming models such as CUDA, OpenCL, and DirectX Compute. The impact of branch divergence can be quite different depending upon whether the program’s control flow is structured or unstructured. In this paper, we show that unstructured control flow occurs frequently in applications and can lead to significant code expansion when executed using existing approaches for handling branch divergence. This paper proposes a new technique for automatically mapping arbitrary control flow onto SIMD processors that relies on a concept of a “Thread Frontier”, which is a statically bounded region of the program
containing all threads that have branched away from the current warp. This technique is evaluated on a GPU emulator configured to model i) a commodity GPU (Intel Sandybridge), and ii) custom hardware support not realized in current GPU architectures. It is shown that this new technique performs identically to the best existing method for structured control flow, and re-converges at the earliest possible point when executing unstructured control flow. This leads to i) between 1.5-633.2% reductions in dynamic instruction counts for several real applications, ii) simplification of the compilation process, and iii) ability to efficiently add high level unstructured programming constructs (e.g., exceptions) to existing data-parallel languages.
(Gregory Diamos, Benjamin Ashbaugh, Subramaniam Maiyuran, Andrew Kerr, Haicheng Wu and Sudhakar Yalamanchili: “SIMD Re-convergence at Thread Frontiers”. 44th International Symposium on Microarchitecture (MICRO 44), 2011. [WWW])