Computation Kernel

Computation kernels are the fundamental building blocks of many algorithms, particularly in machine learning and scientific computing, aiming to optimize the efficiency of core computational tasks. Current research focuses on improving kernel performance across diverse architectures (e.g., GPUs, CPUs, quantum computers) and model types (e.g., transformers, support vector machines), often employing techniques like variance reduction, autotuning, and co-design of hardware and algorithms to minimize memory usage and maximize speed. These advancements are crucial for enabling the deployment of increasingly complex models in resource-constrained environments and accelerating progress in fields ranging from large language models to graph neural networks.