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A Livermore Perspective on Next-Generation Computing

terri

In this video from the Nvidia booth at SC14, Terri Quinn from LLNL presents: A Livermore Perspective on Next-Generation Computing. “Terri is responsible for an organization consisting of three divisions with over 400 technical staff working in high-performance computing, computer security, and enterprise computing. Livermore Computing (LC), LLNL’s high performance computing organization, operates some of the most advanced production classified and unclassified computing environments.”

Video: Energy Secretary Moniz Announces 150 Petaflop Coral Supercomputers

moniz

In this video, U.S. Secretary of Energy Ernest Moniz announces two new High Performance Computing awards to put the nation on a fast-track to next generation exascale computing, which will help to advance U.S. leadership in scientific research and promote America’s economic and national security.

MVAPICH: Still Saving the World – Now Even Faster

Adam Moody, LLNL

“MPI is in the national interest. The U.S. government tasks Lawrence Livermore National Laboratory with solving the nation’s and the world’s most difficult problems. This ranges from global security, disaster response and planning, drug discovery, energy production, and climate change to name a few. To meet this challenge, LLNL scientists utilize large-scale computer simulations on Linux clusters with Infiniband networks. As such, MVAPICH serves a critical role in this effort. In this talk, I will highlight some of this recent work that MVAPICH has enabled.”

Catalyst Supercomputer at Livermore Open for Big Data Innovation

Catalyst2

“The increased storage capacity of the system (in both volatile and nonvolatile memory) represents the major departure from classic simulation-based computing architectures common at DOE laboratories and opens new opportunities for exploring the potential of combining floating point focused capability with data analysis in one environment. The machine’s expanded DRAM and fast, persistent NVRAM are well suited to a broad range of big data problems including bioinformatics, business analytics, machine learning and natural language processing.”