This title appears in the Scientific Report :
2017
Please use the identifier:
http://hdl.handle.net/2128/16012 in citations.
SCIPHI - Score-P and Cube Extensions for Intel Xeon Phi
SCIPHI - Score-P and Cube Extensions for Intel Xeon Phi
The KNL processors offers unique features concerning memory hierarchy and vectorization capabilities. To improve tool support within these two areas, we present extensions to the Score-P measurement system and the Cube report explorer.KNL introduced a new memory architecture, utilizing MCDRAM and DD...
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Personal Name(s): | Feld, Christian (Corresponding author) |
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Schlütter, Marc / Saviankou, Pavel / Knobloch, Michael / Mohr, Bernd | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Imprint: |
2017
|
Conference: | Intel® HPC Developer Conference 2017, Denver, CO (USA), 2017-11-11 - 2017-11-12 |
Document Type: |
Poster |
Research Program: |
Computational Science and Mathematical Methods |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
The KNL processors offers unique features concerning memory hierarchy and vectorization capabilities. To improve tool support within these two areas, we present extensions to the Score-P measurement system and the Cube report explorer.KNL introduced a new memory architecture, utilizing MCDRAM and DDR. To help the user in the decision where to place data structures, we record a MCDRAM candidate metric. In addition we track all MCDRAM allocations through the hbwmalloc API, providing memory metrics like leaked memory or the high-watermark on a per-region basis. For time-line analysis per-process memory statistics are recorded via numastat.KNL's large vector processing unit needs to be utilized and utilized effectively. The metrics compute-to-data access ratio and VPU intensity are introduced to identify vectorization candidates on a per-region basis.Taking the hardware structure into account, the distribution of the KNL-specific metrics is visualized in the Cube report explorer. |