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The Liquid Argon Time Projection Chamber (LArTPC) technology plays anessential role in many current and future neutrino experiments. Accurate andfast simulation is critical to developing efficient analysis algorithms andprecise physics model projections. The speed of simulation becomes moreimportant as Deep Learning algorithms are getting more widely used in LArTPCanalysis and their training requires a large simulated dataset. Heterogeneouscomputing is an efficient way to delegate computing-heavy tasks to specializedhardware. However, as the landscape of the compute accelerators is evolvingfast, it becomes more and more difficult to manually adapt the code constantlyto the latest hardware or software environments. A solution which is portableto multiple hardware architectures while not substantially compromisingperformance would be very beneficial, especially for long-term projects such asthe LArTPC simulations. In search of a portable, scalable and maintainablesoftware solution for LArTPC simulations, we have started to explore high-levelportable programming frameworks that support several hardware backends. In thispaper, we will present our experience porting the LArTPC simulation code in theWire-Cell toolkit to NVIDIA GPUs, first with the CUDA programming model andthen with a portable library called Kokkos. Preliminary performance results onNVIDIA V100 GPUs and multi-core CPUs will be presented, followed by adiscussion of the factors affecting the performance and plans for futureimprovements.

Evaluation of Portable Acceleration Solutions for LArTPC Simulation Using Wire-Cell Toolkit