Visible to Intel only — GUID: GUID-11244C28-8843-49E3-BC66-C1F98A15CA57
Visible to Intel only — GUID: GUID-11244C28-8843-49E3-BC66-C1F98A15CA57
Offload Modeling Accuracy Levels in Command Line
For each perspective, Intel® Advisor has several levels of collection accuracy. Each accuracy level is a set of analyses and properties that control what data is collected and the level of collection details. The higher accuracy value you choose, the higher runtime overhead is added.
You can generate commands for a desired accuracy level from the Intel Advisor GUI. See Generate Command Lines from GUI for details.
CPU-to-GPU Modeling
For the CPU-to-GPU modeling, the following accuracy levels are available:
Comparison / Accuracy Level |
Low |
Medium |
High |
---|---|---|---|
Overhead |
5 - 10x |
15 - 50x |
50 - 80x |
Goal |
Model performance of an application that is mostly compute bound and does not have dependencies |
Model application performance considering memory traffic for all cache and memory levels |
Model application performance with all potential limitations for offload candidates |
Analyses |
Survey + Characterization (Trip Counts and FLOP) + Performance Modeling with no assumed dependencies |
Survey + Characterization (Trip Counts and FLOP with cache simulation for the selected target device, callstacks, and light data transfer simulation) + Performance Modeling with no assumed dependencies |
Survey + Characterization (Trip Counts and FLOP with cache simulation for the selected target device, callstacks, and medium data transfer simulation) + Dependencies + Performance Modeling with assumed dependencies |
Result |
Basic Offload Modeling report that shows potential speedup and performance metrics estimated on a target considering memory traffic from execution units to L1 cache only. The result might be inaccurate for memory-bound applications. |
Offload Modeling report extended with data transfers estimated between host and device platforms considering memory traffic for all cache and memory levels |
Offload Modeling report with detailed data transfer estimations and automated check for loop-carried dependencies for more accurate search for the most profitable regions to offload |
Note: In the commands below, make sure to replace the myApplication with your application executable path and name before executing a command. If your application requires additional command line options, add them after the executable name.
Low Accuracy
To model application performance for with low accuracy for a default target device, run the following command:
advisor --collect=offload --accuracy=low --project-dir=./advi_results -- ./myApplication
This command runs the following analyses one by one:
- Survey analysis:
advisor --collect=survey --auto-finalize --static-instruction-mix --project-dir=./advi_results -- ./myApplication
- Characterization analysis to collect trip count and FLOP data
advisor --collect=tripcounts --flop --auto-finalize --target-device=xehpg_512xve --project-dir=./advi_results -- ./myApplication
- Performance modeling:
advisor --collect=projection --no-assume-dependencies --config=xehpg_512xve --project-dir=./advi_results
Medium Accuracy
This accuracy is set by default. To model application performance with medium accuracy for a default target device, run the following command:
advisor --collect=offload --project-dir=./advi_results -- ./myApplication
This command runs the following analyses one by one:
- Survey analysis:
advisor --collect=survey --auto-finalize --static-instruction-mix --project-dir=./advi_results -- ./myApplication
- Characterization analysis to collect trip count and FLOP data
advisor --collect=tripcounts --flop --stacks --auto-finalize --cache-simulation=single --data-transfer=light --target-device=xehpg_512xve --project-dir=./advi_results -- ./myApplication
- Performance modeling:
advisor --collect=projection --no-assume-dependencies --config=xehpg_512xve --project-dir=./advi_results
High Accuracy
To model application performance with high accuracy for a default target device, run the following command:
advisor --collect=offload --accuracy=high --project-dir=./advi_results -- ./myApplication
This command runs the following analyses one by one:
- Survey analysis:
advisor --collect=survey --auto-finalize --static-instruction-mix --project-dir=./advi_results -- ./myApplication
- Characterization analysis to collect trip count and FLOP data
advisor --collect=tripcounts --flop --stacks --auto-finalize --cache-simulation=single --target-device=xehpg_512xve --data-transfer=medium --project-dir=./advi_results -- ./myApplication
- Dependencies analysis:
advisor --collect=dependencies --filter-reductions --loop-call-count-limit=16 --select markup=gpu_generic --project-dir=./advi_results -- ./myApplication
- Performance modeling:
advisor --collect=projection --config=xehpg_512xve --project-dir=./advi_results
See Check How Dependencies Affect Modeling for a recommended strategy to check for loop-carried dependencies.
GPU-to-GPU Modeling
For the GPU-to-GPU modeling, the following accuracy levels are available:
Comparison / Accuracy Level |
Low |
Medium |
High |
---|---|---|---|
Overhead |
5 - 10x |
15 - 50x |
15 - 50x |
Goal |
Model performance of an application that is mostly compute bound |
Model application performance considering memory traffic for all cache and memory levels |
Model application performance with all potential limitations for offload candidates |
Analyses |
Survey + Characterization (Trip Counts and FLOP) + Performance Modeling |
Survey + Characterization (Trip Counts and FLOP with light data transfer simulation) + Performance Modeling |
Survey + Characterization (Trip Counts and FLOP with medium data transfer simulation) + Performance Modeling |
Result |
Basic Offload Modeling report that shows potential speedup and performance metrics estimated on a target considering memory traffic from execution units to L1 cache only. The result might be inaccurate for memory-bound applications. |
Offload Modeling report extended with data transfers estimated between host and device platforms |
Offload Modeling report with detailed data transfer estimations for more accurate search for the most profitable regions to offload |
Note: In the commands below, make sure to replace the myApplication with your application executable path and name before executing a command. If your application requires additional command line options, add them after the executable name.
Low Accuracy
To model application performance for with low accuracy for a default target device, run the following command:
advisor --collect=offload --accuracy=low --gpu --project-dir=./advi_results -- ./myApplication
This command runs the following analyses one by one:
- Survey analysis:
advisor --collect=survey --auto-finalize --static-instruction-mix --profile-gpu --project-dir=./advi_results -- ./myApplication
- Characterization analysis to collect trip count and FLOP data
advisor --collect=tripcounts --flop --auto-finalize --target-device=xehpg_512xve --profile-gpu --project-dir=./advi_results -- ./myApplication
- Performance modeling:
advisor --collect=projection --no-assume-dependencies --config=xehpg_512xve --profile-gpu --project-dir=./advi_results
Medium Accuracy
This accuracy is set by default. To model application performance with medium accuracy for a default target device, run the following command:
advisor --collect=offload --gpu --project-dir=./advi_results -- ./myApplication
This command runs the following analyses one by one:
- Survey analysis:
advisor --collect=survey --auto-finalize --static-instruction-mix --profile-gpu --project-dir=./advi_results -- ./myApplication
- Characterization analysis to collect trip count and FLOP data
advisor --collect=tripcounts --flop --auto-finalize --data-transfer=light --target-device=xehpg_512xve --profile-gpu --project-dir=./advi_results -- ./myApplication
- Performance modeling:
advisor --collect=projection --no-assume-dependencies --config=xehpg_512xve --profile-gpu --project-dir=./advi_results
High Accuracy
To model application performance with high accuracy for a default target device, run the following command:
advisor --collect=offload --accuracy=high --gpu --project-dir=./advi_results -- ./myApplication
This command runs the following analyses one by one:
- Survey analysis:
advisor --collect=survey --auto-finalize --static-instruction-mix --profile-gpu --project-dir=./advi_results -- ./myApplication
- Characterization analysis to collect trip count and FLOP data
advisor --collect=tripcounts --flop --auto-finalize --target-device=xehpg_512xve --profile-gpu --data-transfer=medium --project-dir=./advi_results -- ./myApplication
- Performance modeling:
advisor --collect=projection --config=xehpg_512xve --profile-gpu --project-dir=./advi_results
After you run the perspective, you can view the results in the Intel Advisor GUI, in CLI, or an interactive HTML report.