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August 11, 2011
This week Kathy Yelick appeared on Intel’s Parallel Computing Talk to discuss the 1.3 petaflop Hopper system at NERSC and to share insights about the programming challenges inherent to petascale and future exascale systems.
Yelick is a professor at the University of California Berkeley, serves as the director of NERSC and is the associate lab director at Lawrence Berkeley National Laboratory. Most of her career has been spent working with parallel programming research projects, compilers, libraries and beyond. At LBNL she has also played a significant role in the high performance cloud computing testbed, Magellan.
Hopper, the research center’s Cray XE6 system, has been tasked with solving a number of problems related to energy use, consumption, and efficiency as well as specific relevant application areas in chemistry, physics and biology. Yelick pointed to a number of noteworthy projects making use of the system, including the 21st Century Reanalysis project, which examines a century of climate conditions and reconstructs these environments to understand and reproduce weather events.
During the interview below (beginning around the 8-minute mark in the video) Yelick gives a thorough overview of the problems that are well-suited for petascale systems and discusses programming Hopper. She says that with around 150 thousand cores, the most popular programming model is a flat MPI model. The system makes use of AMD’s 12-core Magny Cours processors and 2-socket nodes that make 24 cores available within a shared memory space. This allows users to write programs that use some kind of message passing model between different nodes, then threads or OpenMP in the node.
Yelick claims the trend for Hopper is achieve maximum performance by using shared memory directly. The motivation behind this from the user’s standpoint is that the memory footprint is much smaller for a computation that uses threads of some kind. She says that this is important because users are limited by memory in terms of the size of their problems; they want to run a larger problem on the same hardware, thus the choice for a hybrid programming model that uses both MPI and OpenMP.
In a recent solicitation, the NSF laid out needs for furthering its scientific and engineering infrastructure with new tools to go beyond top performance, Having already delivered systems like Stampede and Blue Waters, they're turning an eye to solving data-intensive challenges. We spoke with the agency's Irene Qualters and Barry Schneider about..
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
The Xeon Phi coprocessor might be the new kid on the high performance block, but out of all first-rate kickers of the Intel tires, the Texas Advanced Computing Center (TACC) got the first real jab with its new top ten Stampede system.We talk with the center's Karl Schultz about the challenges of programming for Phi--but more specifically, the optimization...
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
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The Cray CS300-AC cluster supercomputer offers energy efficient, air-cooled design based on modular, industry-standard platforms featuring the latest processor and network technologies and a wide range of datacenter cooling requirements.