Parallel Computing for Accelerating ML Model Performance
Published:
Machine Learning (ML) models have been the backbone of recent advancements in AI. However, as these models continue to grow in size and complexity, so does the demand for computational power and efficiency. This is where parallel computing comes in.
Parallel computing involves the simultaneous execution of multiple tasks and has emerged as a potent tool for accelerating ML model performance. By distributing the workload across multiple processors, parallel computing drastically reduces the time it takes to train and infer from large ML models, enabling faster, more efficient AI systems.
In this blog post, we will delve into the fascinating world of parallel computing and its crucial role in driving the performance of modern ML models.
Check the recent publications:
- M Al Maruf and A Azim. “Optimizing DNNs Model Partitioning for Enhanced Performance on Edge Devices” The 36th Canadian Conference Artificial Intelligence (Canadian AI), Montreal, Canada, 2023.