Abstract
We present an approach to investigate the memory behavior of a parallel kernel executing on thousands of threads simultaneously within the CUDA architecture. Our top-down approach allows for quickly identifying any significant differences between the execution of the many blocks and warps. As interesting warps are identified, we allow further investigation of memory behavior by visualizing the shared memory bank conflicts and global memory coalescence, first with an overview of a single warp with many operations and, subsequently, with a detailed view of a single warp and a single operation. We demonstrate the strength of our approach in the context of a parallel matrix transpose kernel and a parallel 1D Haar Wavelet transform kernel. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 161-170 |
| Number of pages | 10 |
| Journal | Computer Graphics Forum |
| Volume | 32 |
| Issue number | 3pt2 |
| DOIs | |
| State | Published - Jul 1 2013 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: We thank Kristi Potter for her feedback. This work was supported by DOE NETL and KAUST award KUS-C1-016-04.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.