👉 Handjobs, or hand-optimized jobs, refer to computational tasks that are designed to run efficiently on a specific type of hardware, typically GPUs (Graphics Processing Units) or specialized AI accelerators, by leveraging the parallel processing capabilities of these devices. These jobs are optimized to minimize data transfer between the CPU and GPU, maximize memory bandwidth, and exploit the massive parallelism inherent in tasks like matrix multiplications and convolutions common in deep learning. By aligning the software's operations with the hardware's architecture, handjobs can significantly accelerate computations, making them crucial for applications such as image recognition, natural language processing, and generative models. This optimization often involves restructuring algorithms to better fit the hardware's memory hierarchy and communication patterns, ultimately leading to faster execution times and improved performance in AI and machine learning tasks.
