Difference: GPU_inspiral (1 vs. 2)

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GPU_inspiral

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Documentation

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< Ther is some material available on this project:

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There is some material available on this project:

A poster about the project here.
A summary paper here.
Bence's Somhegyi MSc thesis (in hungarian) here.
Bence's Somhegyi MSc thesis presentation (in hungarian) here.

Future plans

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> Instead of writing up a completely new analysis software we will incorporate the code developed as gpu_inspiral to be part of the pyCBC project.

Developers

Bence Somhegyi

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GPU_inspiral

GPU_inspiral is a low-latency, high performance many-core implementation of the matched-filter gravitational wave search algorithm, developed by the RMKI Virgo Group. The base is an architecture and vendor independent OpenCL code. The sub algorithms implemented so far are the following:

template generation
PSD calculation
matched-filtering
chi2 calculation
clustering of events

Performance

Performence test was done on a 2048 sec long data chunk, 500 template of 64 sec long was filtered. The code run on an Nvidia Tesla C2050. To produce the SNR time series it took ~17 sec for gpu_inpiral, this has to be compare with the ~40 minutes necessary for lalapps_inspiral to produce the same result.

This results a *speed-up factor of 2 orders of magnitude !

Documentation

Ther is some material available on this project:

Future plans

Developers

Bence Somhegyi
Gergely Debreczeni

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