Andrés Gómez Tato from CESGA
In this video from the HPC Advisory Council Spain Conference, Andres Gómez Tato from CESGA presents: HPC Meets Machine Learning.
“Machine learning is a key topic in almost any area of computer science and technology. With the explosion of Deep Learning thanks to the availability of large volume of data, computational resources are needed to train large models, using GPUs and distributed computing. When working on large models, HPC infrastructures can help to speed-up some task during the model design and training. Based on the experience at CESGA and FORTISIMO, this talk reviews the computational needs of Deep Learning, the use cases where HPC can help to Machine Learning, the performance of available Machine Learning APIs and the parallel methods commonly used during ML training.”
