The Data Science with Spark Workshop addresses high-level parallelization for data analytics workloads using the Apache Spark framework. Participants will learn how to prototype with Spark and how to exploit large HPC machines like the Piz Daint CSCS flagship system.
In this video, Ricard Borrell from the Barcelona Supercomputing Center describes how the Mont Blanc Project Industrial End User Group on TermoFluids is advancing HPC on ARM-based platforms.
“This video is from the opening session of the “Introduction to Programming Pascal (P100) with CUDA 8″ workshop at CSCS in Lugano, Switzerland. The three-day course is intended to offer an introduction to Pascal computing using CUDA 8.”
“GPUs potentially offer exceptionally high memory bandwidth and performance for a wide range of applications. The challenge in utilizing such accelerators has been the difficulty in programming them. Enter GPU Hackathons; Our mentors come from national laboratories, universities and vendors, and besides having extensive experience in programming GPUs, many of them develop the GPU-capable compilers and help define standards such as OpenACC and OpenMP.”
“Do you need to compress your software development cycles for services deployed at scale and accelerate your data-driven insights? Are you delivering solutions that automate decision making & model complexity using analytics and machine learning on Spark? Find out how a pre-integrated analytics platform that’s tuned for memory-intensive workloads and powered by the industry leading interconnect will empower your data science and software development teams to deliver amazing results for your business. Learn how Cray’s supercomputing approach in an enterprise package can help you excel at scale.”
“Electricity transformed industries: agriculture, transportation, communication, manufacturing. I think we are now in that phase where AI technology has advanced to the point where we see a clear path for it to transform multiple industries.” Specifically, Ng sees AI being particularly influential in entertainment, retail, and logistics.
In this video, Dr. Marcelo Ponce from SciNet presents: Scientific Visualization with Python. “SciNet is Canada’s largest supercomputer centre, providing Canadian researchers with computational resources and expertise necessary to perform their research on scales not previously possible in Canada. We help power work from the biomedical sciences and aerospace engineering to astrophysics and climate science.”
“This talk will describe Monotasks, a new architecture for the core of Spark that makes performance easier to reason about. In Spark today, pervasive parallelism and pipelining make it difficult to answer even simple performance questions like “what is the bottleneck for this workload?” As a result, it’s difficult for developers to know what to optimize, and it’s even more difficult for users to understand what hardware to use and what configuration parameters to set to get the best performance.”
In this video from the 2017 HPC Advisory Council Stanford Conference, Yifan Zhang from Stanford presents: Using HPC in a Cohort Study of the Health Effects of Handgun Ownership in California. Researchers hope the study will identify characteristics of individuals at highest risk of experiencing firearm-related mortality.
In this slidecast, Jem Davies (VP Engineering and ARM Fellow) gives a brief introduction to Machine Learning and explains how it is used in devices such as smartphones, autos, and drones. “I do think that machine learning altogether is probably going to be one of the biggest shifts in computing that we’ll see in quite a few years. I’m reluctant to put a number on it like — the biggest thing in 25 years or whatever,” said Jem Davies in a recent investor call. “But this is going to be big. It is going to affect all of us. It affects quite a lot of ARM, in fact.”