Several critical human activities depend on the weather forecasting. Some of them are transportation, health, work, safety, and agriculture. Such activities require computational solutions for weather forecasting through numerical models. These numerical models must be accurate and allow the computers to process them quickly. In this project, we aim at migrating a small part of the software of the weather forecasting model of Brazil, BRAMS-Brazilian developments on the Regional Atmospheric Modelling System-to a heterogeneous system composed of Xeon (Intel) processors coupled to a reprogrammable circuit (FPGA) via PCIe bus. According to the studies in the literature, the chemical equation from the mass continuity equation is the most computationally demanding part. This term calculates several linear systems Ax = b. Thus, we implemented such equations in hardware and provided a portable and highly parallel design in OpenCL language. The OpenCL framework also allowed us to couple our circuit to BRAMS legacy code in Fortran90. Although the development tools present several problems, the designed solution has shown to be viable with the exploration of parallel techniques. However, the performance was below of what we expected.

In this article, we focus on the acceleration of a chemical reaction simulation that relies on a system of stiff ordinary differential equation (ODEs) targeting heterogeneous computing systems with CPUs and field-programmable gate arrays (FPGAs). Specifically, we target an essential kernel of the coupled chemistry aerosol-tracer transport model to the Brazilian developments on the regional atmospheric modeling system (CCATT-BRAMS). We focus on a linear solve step using the QR factorization based on the modified Gram-Schmidt method as the basis of the ODE solver in this application. We target Intel hardware accelerator research program (HARP) architecture with the OpenCL programming environment for these early experiments. Our design exploration reveals a hardware design that is up to 4 times faster than the original iterative Jacobi method used in this solver. Still, even with hardware support, the overall performance of our QR-based hardware is lower than its original software version.

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