Multiphase turbulent flow phenomena are observed not only in industrial devices but also in environmental flows, and direct numerical simulation (DNS) plays a key role in their investigation. Many numerical models have been developed; nevertheless, few models are highly optimized for GPU platforms, which represent the current mainstream in high-performance computing (HPC). In this study, we developed LCS.jl (Lagrangian Cloud Simulator in Julia), a single-source and multi-platform multiphase turbulence simulation model implemented in Julia language and KernelAbstractions.jl. Validation results confirmed that the present fluid and particle statistics agree well with those obtained in prior studies. A GPU-native particle communication algorithm based on prefix-scan reduced the particle communication cost from approximately 78% (CPU-delegated) to 10% of total execution time. LCS.jl achieved computational performance equivalent to the Fortran implementation in many-processes computations. For GPUs, strong scaling efficiency was maintained above 85% (up to 256 GPUs) and weak scaling efficiency above 90% (up to 216 GPUs) on TSUBAME4.0 (a GPU supercomputer at the Institute of Science Tokyo). LCS.jl achieved a maximum speedup of 18.0x on GPUs over CPUs. A trial heterogeneous execution achieved a 72% reduction in execution time compared to the CPU-only configuration even in configurations where the GPU was not the primary compute device. These results demonstrate that LCS.jl is a multiphase turbulence simulation platform that achieves both portability and scalability across a variety of computational resource configurations.

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