“Computational science has come a long way with machine learning (ML) and deep learning (DL) in just the last year. Leading centers of high-performance computing are making great strides in developing and running ML/DL workloads on their systems. Users and algorithm scientists are continuing to optimize their codes and techniques that run their algorithms, while system architects work out the challenges they still face on various system architectures. At SC16, I had the honor of hosting three of HPC’s thought leaders in a panel to get their ideas about the state of Artificial Intelligence (AI), today’s challenges with the technology, and where it’s going.”
“In recent years, major breakthroughs were achieved in different fields using deep learning. From image segmentation, speech recognition or self-driving cars, deep learning is everywhere. Performance of image classification, segmentation, localization have reached levels not seen before thanks to GPUs and large scale GPU-based deployments, leading deep learning to be a first class HPC workload.”
“In this guide, we take a high-level view of AI and deep learning in terms of how it’s being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.”
In this guide we explain the difference between AI, machine learning and deep learning, and includes highlights of the insideBIGDATA audience survey. To learn more about AI and deep learning download this guide.
“Professional workflows are now infused with artificial intelligence, virtual reality and photorealism, creating new challenges for our most demanding users,” said Bob Pette, vice president of Professional Visualization at NVIDIA. “Our new Quadro lineup provides the graphics and compute performance required to address these challenges. And, by unifying compute and design, the Quadro GP100 transforms the average desktop workstation with the power of a supercomputer.”
“Billed as an exposition into ‘The Future of Cloud HPC Simulation,’ the event brought together experts in high-performance computing and simulation, cloud computing technologists, startup founders, and VC investors across the technology landscape. In addition to product demonstrations with Rescale engineers, including the popular Deep Learning workshop led by Mark Whitney, Rescale Director of Algorithms, booths featuring ANSYS, Microsoft Azure, Data Collective, and Microsoft Ventures offered interactive sessions for attendees.”
Dr. Amit Seti from IIT-Gauwhati presented this talk at GTCx in India. “This talk will cover how medical imaging data can be used to train computer vision systems that automate diagnostic analysis in current clinical practice. Not only that, with more creative use of data, we can go even beyond that to predict outcome of specific treatment for individual patients. We will cover results from prostate and breast cancers to show that a future is not too far where algorithms will become a necessary set of tools in a pathologist’s toolbox.”
Today Intel announced record results on a new benchmark in deep learning and convolutional neural networks (CNN). ZTE’s engineers used Intel’s midrange Arria 10 FPGA for a cloud inferencing application using a CNN algorithm. “ZTE has achieved a new record – beyond a thousand images per second in facial recognition – with what is known as “theoretical high accuracy” achieved for their custom topology. Intel’s Arria 10 FPGA accelerated the raw design performance more than 10 times while maintaining the accuracy.”
Today IBM announced that its PowerAI distribution for popular open source Machine Learning and Deep Learning frameworks on the POWER8 architecture now supports the TensorFlow 0.12 framework that was originally created by Google. TensorFlow support through IBM PowerAI provides enterprises with another option for fast, flexible, and production-ready tools and support for developing advanced machine learning products and systems.
“CUDA C++ is just one of the ways you can create massively parallel applications with CUDA. It lets you use the powerful C++ programming language to develop high performance algorithms accelerated by thousands of parallel threads running on GPUs. Many developers have accelerated their computation- and bandwidth-hungry applications this way, including the libraries and frameworks that underpin the ongoing revolution in artificial intelligence known as Deep Learning.”