GPU Accelerated Molecular Dynamics with Method of Heterogeneous Load Balancing

Molecular Dynamics simulations are widely used to obtain a deeper understanding of chemical reactions, fluid flows, phase transitions, and other physical phenomena due to molecular interactions. The main problem with this method is that it is computationally demanding because of its amount of O (N2) and requirements for prolonged simulations. The use of Graphics Processing Units (GPUs) is an attractive solution and has been applied to this problem thus far. However, such heterogeneous approaches occasionally cause load imbalances between CPUs and GPUs and they don't utilize all computational resources. We propose a method of balancing the workload between CPUs and GPUs, which we implemented. Our method is based on formulating and observing workloads and it statically distributes work according to spatial decomposition. We succeeded in utilizing processors more efficiently and accelerating simulations by 20.7 % at most compared to the original GPU optimized code.