👉 Suspension computing is an advanced computational technique designed to enhance the efficiency and performance of large-scale machine learning models, particularly those involving extensive memory usage. At its core, suspension computing allows for the seamless pausing and resuming of computations, enabling systems to dynamically allocate resources based on current workload demands. This is achieved through a sophisticated scheduling mechanism that identifies and isolates memory-intensive operations, suspending them while other parts of the computation continue uninterrupted. When the workload decreases, the system can then resume these paused operations without losing progress, thus minimizing data loss and reducing overall computation time. This approach is especially beneficial for training deep neural networks, where memory bottlenecks can significantly slow down the process. By optimizing resource utilization and maintaining computational continuity, suspension computing offers a promising solution to the challenges posed by increasingly complex AI models.
