Control Flow Coalescing On A Hybrid Dataflow/Von Neumann Gpgpu

We propose the hybrid dataflow/von Neumann vector graph instruction word (VGIW) architecture. This data-parallel architecture concurrently executes each basic block's dataflow graph (graph instruction word) for a vector of threads, and schedules the different basic blocks based on von Neumann control flow semantics. The VGIW processor dynamically coalesces all threads that need to execute a specific basic block into a thread vector and, when the block is scheduled, executes the entire thread vector concurrently. The proposed control flow coalescing model enables the VGIW architecture to overcome the control flow divergence problem, which greatly impedes the performance and power efficiency of data-parallel architectures. Furthermore, using von Neumann control flow semantics enables the VGIW architecture to overcome the limitations of the recently proposed single-graph multiple-flows (SGMF) dataflow GPGPU, which is greatly constrained in the size of the kernels it can execute. Our evaluation shows that VGIW can achieve an average speedup of 3x (up to 11x) over an NVIDIA GPGPU, while providing an average 1.75x better energy efficiency (up to 7x).