At the Oak Ridge Leadership Computing Facility (OLCF), scientists and engineers run programs, or application codes, on America's fastest supercomputer. Called Titan, the supercomputer is a marvel of engineering with 18,688 graphic processing units and 299,008 central processing unit (CPU) cores working in concert at a peak speed of 27 quadrillion calculations per second. The codes task the processors to model hurricanes and earthquakes that put people and property at risk, simulate combustion instabilities in power plant turbines and vehicle engines, predict properties of advanced materials, and spur other discoveries and innovations. Titan accelerates breakthroughs in fields in which major advances would not be probable or possible without massively parallel computing.
That said, researchers usually don't begin their computations on Titan. They often start with scaled-down problems run on computing clusters at their home institutions or on partitions of supercomputers that provide access to hundreds of processors at most. They add detail to their models to achieve greater realism, and eventually they need to run the simulation on as many processors as possible to achieve the requisite resolution and complexity. They come to a leadership-class computing facility to do so. At the OLCF they may use a supercomputer with 100 times as many processors as they've used before, and they're expecting their code to run 100 times as fast. But sometimes they find the code runs just three times as fast. What went wrong?
Computer scientists have developed tools to diagnose the millions of things that can go wrong when a program runs through its routines. In
"Understanding code behavior at this new scale with Vampir is huge," said ORNL computer scientist
The research team first presented its findings in
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