Fast Simulation of Conway’s Game of Life Using Bitwise Parallel Bulk Computation on a GPU

Abstract

Conway’s Game of Life is the most well-known cellular automaton. The universe of the Game of Life is a 2-dimensional array of cells, each of which takes two possible states, alive or dead. The state of every cell is repeatedly updated according to those of eight neighbors. A cell will be alive if exactly three neighbors are alive, or if it is alive and two neighbors are alive. The main contribution of this paper is to develop several acceleration techniques for simulating the Game of Life using a GPU as follows: (1) the states of 32/64 cells in 32/64-bit words (integers) and the next states are computed by the Bitwise Parallel Bulk Computation (BPBC) technique, (2) the states of cells stored in 2 words are updated at the same time by a thread, (3) warp shuffle instruction is used to directly transfer the current states stored in registers, and (4) multi-step simulation is performed to reduce the overhead of data transfer and invoking CUDA kernel. The experimental results show that, the performance of our GPU implementation using GeForce GTX TITAN X is 1350×109 updates per second for 16K-step simulation of 512K ×512K cells stored in the SSD. Since Intel Core i7 CPU using the same technique performs 13.4×109 updates per second, our GPU implementation for the Game of Life achieves a speedup factor of 100. Thus, these techniques work very efficiently on a GPU.