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Artificial intelligence (AI) is transforming research through analysis of massive datasets and accelerating simulations by factors of up to a billion. Such acceleration eclipses the speedups that were made possible though improvements in CPU process and design and other kinds of algorithmic advances. It sets the stage for a new era of discovery in which previously intractable challenges will become surmountable, with applications in fields such as discovering the causes of cancer and rare diseases, developing effective, affordable drugs, improving food sustainability, developing detailed understanding of environmental factors to support protection of biodiversity, and developing alternative energy sources as a step toward reversing climate change. To succeed, the research community requires a high-performance computational ecosystem that seamlessly and efficiently brings together scalable AI, general-purpose computing, and large-scale data management. The authors, at the Pittsburgh Supercomputing Center (PSC), launched a second-generation computational ecosystem to enable AI-enabled research, bringing together carefully designed systems and groundbreaking technologies to provide at no cost a uniquely capable platform to the research community. It consists of two major systems: Neocortex and Bridges-2. Neocortex embodies a revolutionary processor architecture to vastly shorten the time required for deep learning training, foster greater integration of artificial deep learning with scientific workflows, and accelerate graph analytics. Bridges-2 integrates additional scalable AI, high-performance computing (HPC), and high-performance parallel file systems for simulation, data pre- and post-processing, visualization, and Big Data as a Service. Neocortex and Bridges-2 are integrated to form a tightly coupled and highly flexible ecosystem for AI- and data-driven research. 

Artificial intelligence (AI) has immense potential spanning research and industry. AI applications abound and are expanding rapidly, yet the methods, performance, and understanding of AI are in their infancy. Researchers face vexing issues such as how to improve performance, transferability, reliability, comprehensibility, and how better to train AI models with only limited data. Future progress depends on advances in hardware accelerators, software frameworks, system and architectures, and creating cross-cutting expertise between scientific and AI domains. Open Compass is an exploratory research project to conduct academic pilot studies on an advanced engineering testbed for artificial intelligence, the Compass Lab, culminating in the development and publication of best practices for the benefit of the broad scientific community. Open Compass includes the development of an ontology to describe the complex range of existing and emerging AI hardware technologies and the identification of benchmark problems that represent different challenges in training deep learning models. These benchmarks are then used to execute experiments in alternative advanced hardware solution architectures. Here we present the methodology of Open Compass and some preliminary results on analyzing the effects of different GPU types, memory, and topologies for popular deep learning models applicable to image processing.