A Gentle Introduction to GPU Computing

As data science continues to mature and evolve, the demand for more computationally extensive machines is rising. GPU Computing provides the core capabilities that data scientists today are looking for, and when implemented effectively, it accelerates deep learning, analytics and other sophisticated engineering applications. During this talk, Armen Donigian, Data Science Engineer at ZestFinance, will introduce the GPU programming model and parallel computing patterns, as well as practical implications of GPU computing, such as how to accelerate applications on a GPU with CUDA (C++/Python), GPU memory optimizations and multi GPU programming with MPI and OpenACC. As an example of how GPU programming can be implemented in real-life business models, Armen will present how ZestFinance has successfully tapped into the power of GPU Computing for the deep learning algorithm behind its new platform, Zest Automated Machine Learning platform (ZAML). Currently, ZAML is used by major tech, credit and auto companies to successfully apply cutting-edge machine learning models to their toughest credit decisioning problems. ZAML leverages GPU Computing for data parallelism, model parallelism and training parallelism.