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The Trotter-Suzuki approximation leads to an efficient algorithm for solvingthe time-dependent Schr\"odinger equation. Using existing highly optimized CPUand GPU kernels, we developed a distributed version of the algorithm that runsefficiently on a cluster. Our implementation also improves single nodeperformance, and is able to use multiple GPUs within a node. The scaling isclose to linear using the CPU kernels, whereas the efficiency of GPU kernelsimprove with larger matrices. We also introduce a hybrid kernel thatsimultaneously uses multicore CPUs and GPUs in a distributed system. Thiskernel is shown to be efficient when the matrix size would not fit in the GPUmemory. Larger quantum systems scale especially well with a high number nodes.The code is available under an open source license.

A Second-Order Distributed Trotter-Suzuki Solver with a Hybrid Kernel