Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Concept
Titan (supercomputer)
Summary
Titan or OLCF-3 was a supercomputer built by Cray at Oak Ridge National Laboratory for use in a variety of science projects. Titan was an upgrade of Jaguar, a previous supercomputer at Oak Ridge, that uses graphics processing units (GPUs) in addition to conventional central processing units (CPUs). Titan was the first such hybrid to perform over 10 petaFLOPS. The upgrade began in October 2011, commenced stability testing in October 2012 and it became available to researchers in early 2013. The initial cost of the upgrade was US$60 million, funded primarily by the United States Department of Energy.
Titan was eclipsed at Oak Ridge by Summit in 2019, which was built by IBM and features fewer nodes with much greater GPU capability per node as well as local per-node non-volatile caching of file data from the system's .
Titan employed AMD Opteron CPUs in conjunction with Nvidia Tesla GPUs to improve energy efficiency while providing an order of magnitude increase in computational power over Jaguar. It used 18,688 CPUs paired with an equal number of GPUs to perform at a theoretical peak of 27 petaFLOPS; in the LINPACK benchmark used to rank supercomputers' speed, it performed at 17.59 petaFLOPS. This was enough to take first place in the November 2012 list by the TOP500 organization, but Tianhe-2 overtook it on the June 2013 list.
Titan was available for any scientific purpose; access depends on the importance of the project and its potential to exploit the hybrid architecture. Any selected programs must also be executable on other supercomputers to avoid sole dependence on Titan. Six vanguard programs were the first selected. They dealt mostly with molecular scale physics or climate models, while 25 others were queued behind them. The inclusion of GPUs compelled authors to alter their programs. The modifications typically increased the degree of parallelism, given that GPUs offer many more simultaneous threads than CPUs. The changes often yield greater performance even on CPU-only machines.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.