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Tuesday, July 11 • 2:00pm - 2:30pm
A buffering approach to manage I/O in a normalized cross-correlation earthquake detection code for large seismic datasets

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Continued advances in high-performance computing architectures constantly move the computational performance forward widening performance gap with I/O.
As a result, I/O plays an increasingly critical role in modern data-intensive scientific applications.

We have developed a high-performance GPU-based software called \textit{cuNCC}, which is designed to calculate seismic waveform similarity for subjects like hypocenter estimates and small earthquake detection. GPU's
acceleration greatly reduced the compute time and we are currently investigating I/O optimizations, to tackle this new performance bottleneck.

In order to find an optimal I/O solution for our \textit{cuNCC} code, we had performed a series of I/O benchmark tests and implemented buffering in CPU
memory to manage the output transfers. With this preliminary work, we were able to establish that buffering improves the I/O bandwidth achieved, but is only
beneficial when I/O bandwidth is limited, since the cost of the additional memory copy may exceed improvement in I/O. However, in the realistic environment
where I/O bandwidth per node is limited, and small I/O transfers are penalized, this technique will improve overall performance. In addition, by using a large
memory system, the point at which computing has to stop to wait for I/O is delayed, enabling fast computations on larger data sets.


Tuesday July 11, 2017 2:00pm - 2:30pm
Bolden 5
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