Georgia Tech’s College of Engineering has established an artificial intelligence supercomputer hub dedicated exclusively to teaching students. The initiative — the AI Makerspace — was launched in collaboration with NVIDIA. College leaders call it a digital sandbox for students.... 

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In this interview, we hear from two leaders of Women in HPC, the worldwide organization that encourages, mentors and supports women and addresses gender issues in the high performance computing industry. Speaking for the organization are Lorna Rivera, research scientist in program evaluation at the Georgia Tech Center for Education Integrating Science, Mathematics and Computing […]

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The Association for Computing Machinery (ACM) and IEEE Computer Society (IEEE CS) have named Vivek Sarkar of Georgia Institute of Technology winner of the 2020 ACM/IEEE CS Ken Kennedy Award. Sarkar is recognized for “foundational technical contributions to the area of programmability and productivity in parallel computing, as well as leadership contributions to professional service, mentoring, […]

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This year’s Student Cluster Competition at SC20 will include two firsts: it will involve the most number of teams (19) in the competition’s 14-year history, and it will for the first time be held completely virtually. “This year’s Student Cluster Competition will be very different, as it will be 100 percent cloud-based,” explained SC20 SCC […]

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Intel and the National Science Foundation (NSF), joint funders of the Machine Learning for Wireless Networking Systems (MLWiNS) program, today announced recipients of awards for research projects into ultra-dense wireless systems that deliver the throughput, latency and reliability requirements of future applications – including distributed machine learning computations over wireless edge networks. Here are the […]

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In this video, Oded Green from NVIDIA unboxes a DGX-1 supercomputer at the College of Computing Data Center at Georgia Tech. "And while the DGX-1 arriving at Georgia Tech for student-use is exciting enough, there is cause for more celebration as a DGX Station also arrived this year as part of a new NVIDIA Artificial Intelligence Lab (NVAIL) grant awarded to CSE. The NVAIL grant focuses on developing multi-GPU graph analytics and the DGX station is constructed specifically for data science and artificial intelligence development."

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In this video from PASC18, David Bader from Georgia Tech summarizes his keynote talk on Big Data Analytics. "Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; and improving the resilience of the electric power grid. Unlike traditional applications in computational science and engineering, solving these social problems at scale often raises new challenges because of the sparsity and lack of locality in the data, the need for research on scalable algorithms, and development of frameworks for solving these real-world problems on high performance computers."

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The PASC18 conference has posted their conference speaker agenda. Registration is now open for this HPC event, which takes place July 2-4 in Basel, Switzerland. "PASC18 offers three days of stimulating technical sessions with more than 200 talks in total. The program includes keynote presentations, minisymposia, peer-reviewed papers, posters, an interdisciplinary dialogue, and a panel discussion."

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Today PASC18 announced that this year’s Public Lecture will be held by David Bader from Georgia Tech. Dr. Bader will speak on Massive-Scale Analytics Applied to Real-World Problems. "Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; and improving the resilience of the electric power grid. Unlike traditional applications in computational science and engineering, solving these social problems at scale often raises new challenges because of the sparsity and lack of locality in the data, the need for research on scalable algorithms and development of frameworks for solving these real-world problems on high performance computers, and for improved models that capture the noise and bias inherent in the torrential data streams. This talk will discuss the opportunities and challenges in massive data-intensive computing for applications in social sciences, physical sciences, and engineering."

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Scientists typically understand data through graphs and visualizations. But is it possible to use sound to interpret complex information? This video from Georgia Tech's Asegun Henry shows the Sonification of the vibrations of an atom in crystalline silicon. "If you look at the data, it looks like white noise," Henry said. "We decided to sonify the data, and as soon as we listened to it, we could hear the pattern."

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