Fine-Grained Synchronizations and Dataflow Programming on GPUs

The last decade has witnessed the blooming emergence of many-core platforms, especially the graphic processing units (GPUs). With the exponential growth of cores in GPUs, utilizing them efficiently becomes a challenge. The data-parallel programming model assumes a single instruction stream for multiple concurrent threads (SIMT); therefore little support is offered to enforce thread ordering and fine-grained synchronizations. This becomes an obstacle when migrating algorithms which exploit fine-grained parallelism, to GPUs, such as the dataflow algorithms. In this paper, we propose a novel approach for fine-grained inter-thread synchronizations on the shared memory of mod-ern GPUs. We demonstrate its performance and compare it with other fine-grained and medium-grained synchroniza-tion approaches. Our method achieves 1.5x speedup over the warp-barrier based approach and 4.0x speedup over the atomic spin-lock based approach on average. To further ex-plore the possibility of realizing fine-grained dataflow algo-rithms on GPUs, we apply the proposed synchronization scheme to Needleman-Wunsch – a 2D wavefront application involving massive cross-loop data dependencies. Our imple-mentation achieves 3.56x speedup over the atomic spin-lock implementation and 1.15x speedup over the conventional data-parallel implementation for a basic sub-grid, which im-plies that the fine-grained, lock-based programming pattern could be an alternative choice for designing general-purpose GPU applications (GPGPU)