The nature of many applications is changing to become more data intensive as the number of data points that must be processed multiplies. In addition, many algorithms need to perform the same calculations on each data point in large data sets, introducing the opportunity for performing these calculations in parallel. The need for parallel computing became obvious with the work of the AlexNet team winning the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and Google’s Brain Team advancing the science of artificial intelligence (AI) and machine learning (ML) and providing an open source machine learning library for neural network-based ML called TensorFlow. Other examples of applications needing parallel computation include advanced driver assistance systems (ADAS) used in self-driving cars and real-time rendering for virtual reality (VR), augmented reality (AR), climate analysis, and financial trend analysis. Clients, please log in to read the full insight.