Designing large-scale geological carbon capture and storage projects and ensuring safe long-term CO2 containment - as a climate change mitigation strategy - requires fast and accurate numerical simulations. These simulations involve solving complex PDEs governing subsurface fluid flow using implicit finite-volume schemes widely based on Two-Point Flux Approximation (TPFA). This task is computationally and memory expensive, especially when performed on highly detailed geomodels. In most current HPC architectures, memory hierarchy and data management mechanism are insufficient to overcome the challenges of large scale numerical simulations. Therefore, it is crucial to design algorithms that can exploit alternative and more balanced paradigms, such as dataflow and in-memory computing. This work introduces an algorithm for TPFA computations that leverages effectively a dataflow architecture, such as Cerebras CS2, which helps to significantly minimize memory bottlenecks. Our implementation achieves two orders of magnitude speedup compared to multiple reference implementations running on NVIDIA A100 GPUs.

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