Preface

Hello there! I’m a system analyst and academic professor specializing in High-Performance Computing (HPC). Yes, you read it right! I’m not a data scientist. So, you are probably wondering why on Earth I decided to write a book about machine learning. Don’t worry; I will explain.

HPC systems comprise powerful computing resources tightly integrated to solve complex problems. The main goal of HPC is to employ resources, techniques, and methods to accelerate the execution of highly intensive computing tasks. Traditionally, HPC environments have been used to execute scientific applications from biology, physics, chemistry, and many other areas.

But this has changed in the past few years. Nowadays, HPC systems run tasks beyond scientific applications. In fact, the most prominent non-scientific workload executed in HPC environments is precisely the subject of this book: the building process of complex neural network models.

As a data scientist, you know better than anyone else how long it could take to train complex models and how many times you need to retrain the model to evaluate different scenarios. For this reason, the usage of HPC systems to accelerate Artificial Intelligence (AI) applications (not only for training but also for inference) is a growth-demanding area.

This close relationship between AI and HPC sparked my interest in diving into the fields of machine learning and AI. By doing this, I could better understand how HPC has been applied to accelerate these applications.

So, here we are. I wrote this book to share what I have learned about this topic. My mission here is to give you the necessary knowledge to train your model faster by employing optimization techniques and methods using single or multiple computing resources.

By accelerating the training process, you can concentrate on what really matters: building stunning models!