Kernel Density Visualization (KDV) has been extensively used in a wide range of applications, including traffic accident hotspot detection, crime hotspot detection, disease outbreak detection, and ecological modeling. However, KDV is a computationally expensive operation, which is not scalable to large datasets (e.g., million-scale data points) and high resolution sizes (e.g., 1920 x 1080). To significantly improve the efficiency for generating KDV, we develop two efficient Sweep Line AlgorithMs (SLAM), which can theoretically reduce the time complexity for generating KDV. By incorporating the resolution-aware optimization (RAO) into SLAM, we can further achieve the lowest time complexity for generating KDV. Our extensive experiments on four large-scale real datasets (up to 4.33 million data points) show that all our methods can achieve one to two-order-of-magnitude speedup in many test cases and efficiently support KDV with exploratory operations (e.g., zooming and panning) compared with the state-of-the-art solutions.