Dynamic Per-Warp Reconvergence Stack for Efficient Control Flow Handling in GPUs

Abstract

GPGPUs usually experience performance degradation when the control flow of threads diverges in a warp. Reconvergence stack based control flow handling scheme is widely adopted in GPU architectures. The depth of such stack is always set to a large number, so that there can be enough entries for warps experiencing nested branches. However, for warps experiencing simple branches or even no branches, those deep reconvergence stacks would stay idle, causing a serious waste of hardware resource. Moreover, with the development of GPU architectures, more and more warps will be deployed on a GPU stream processor core, such problem could be even more serious. To solve this problem, this paper propose a dynamic reconvergence stack structure, in which a stack pool is shared by all the warps, and dynamic stacks of different warps can be constructed according to the run-time requirement. This can satisfy the stack requirement while eliminating unnecessary waste of hardware resource. Our experiments show that the dynamic reconvergence stack can reduce the cost of stack by 50% with the conventional performance well maintained.