byteLAKE's presentation from the PPAM 2019 conference. Abstract: The goal of this work is to adapt 4 CFD kernels to the Xilinx ALVEO U250 FPGA, including first-order step of the non-linear iterative upwind advection MPDATA schemes (non-oscillatory forward in time), the divergence part of the matrix-free linear operator formulation in the iterative Krylov scheme, tridiagonal Thomas algorithm for vertical matrix inversion inside preconditioner for the iterative solver, and computation of the psuedovelocity for the second pass of upwind algorithm in MPDATA. All the kernels use 3-dimensional compute domain consisted from 7 to 11 arrays. Since all kernels belong to the group of memory bound algorithms, our main challenge is to provide the highest utilization of global memory bandwidth. Our adaptation allows us to reduce the execution time upto 4x. Find out more at: www.byteLAKE.com/en/CFD Foot note: This is the presentation about the non-AI version of byteLAKE's CFD kernels, highly optimized for Alveo FPGA. Based on this research project and many others in the CFD space, we decided to shift the course of the CFD Suite product development and leverage AI to accelerate computations and enable new possibilities. Instead of adapting CFD solvers to accelerators, we use AI and work on a cross-platform solution. More on the latest: www.byteLAKE.com/en/CFDSuite. - Update for 2020: byteLAKE is currently developing CFD Suite as AI for CFD Suite, a collection of AI/ Artificial Intelligence Models to accelerate and enable new features for CFD simulations. It is a cross-platform solution (not only for FPGAs). More: www.byteLAKE.com/en/CFDSuite.