GPU Kernel

GPU kernels are optimized code segments crucial for maximizing the performance of parallel computations on graphics processing units (GPUs). Current research focuses on automating the often complex process of tuning kernel parameters, employing techniques like deep sequence-to-sequence models and Bayesian optimization to efficiently explore the vast design space and predict optimal configurations. These advancements are significantly impacting various fields, from accelerating machine learning model training (e.g., Graph Neural Networks) and scientific computing to improving data compression and handling dynamic sparsity in deep learning, ultimately leading to faster and more efficient computations.