cpu-gpu collaboration for output quality monitoring
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CPU-GPU Collaboration for Output Quality Monitoring. Mehrzad Samadi and Scott Mahlke University of Michigan March 2014. University of Michigan Electrical Engineering and Computer Science. Compilers creating custom processors. Output Quality Monitoring. Sampling over time - PowerPoint PPT PresentationTRANSCRIPT
CPU-GPU Collaboration for Output Quality Monitoring
Mehrzad Samadi and Scott Mahlke
University of MichiganMarch 2014
Compilers creating custom processorsUniversity of MichiganElectrical Engineering and Computer Science
2
Output Quality Monitoring• Sampling over time•Green[PLDI2010], SAGE[MICRO2013]
• Works fine for applications with temporal similarity for example video processing• What about applications without temporal similarity?
Quality
TOQTOQ + delta
TOQ - delta
Check the quality
3
Output Quality Monitoring
• Sampling over time
• Sampling over space
4
Partial Output Quality Monitoring
Accurate Version
ApproximateVersion
EvaluationMetric
Subset of Input Data
5
CCG• Collaborative CPU-GPU Output Quality Monitoring
Approximate Run 0GPU
CPU
Approximate Run 1
Approximate Run 2
Approximate Run 3
Check 1 Check 2 Check 3 Check 4
Decision Decision Decision
• CPU performs the monitoring while GPU is executing the approximate code
6
Evaluation• Two Image processing applications:• Mosaic• Mean Filter
• 1600 flower images• NVIDIA GTX 560 + Intel Core i7• CCG: Collaborative CPU-GPU approach
Time Sampling Conservative
Aggressive AdaptiveFixed
AdaptiveFixed CFI
CAI
AFIAAI
7
Conservative/ Aggressive
Quality
TOQTOQ + delta
TOQ - delta
Speedup
Conservative
Aggressive
8
Results
CFI
CAI
AFI
AAI
CCG
0 10 20 30 40 50 60
Mosaic Mean
Percentage of images with unacceptable quality
CFI
CAI
AFI
AAI
CCG
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8
Speedup
9
Conclusions• Sampling over time is not the answer for all applications
• We need to check all invocations for most of the applications
• Full quality monitoring has really high overhead
• Partial quality monitoring can be a solution9
CPU-GPU Collaboration for Output Quality Monitoring
Mehrzad Samadi and Scott Mahlke
University of MichiganMarch 2014
Compilers creating custom processorsUniversity of MichiganElectrical Engineering and Computer Science
11
Fixed/Adaptive• Fixed
• Adaptive: Reduce the overhead of checking.
Quality
TOQTOQ + delta
TOQ - delta
Quality
TOQTOQ + delta
TOQ - delta
12
Results
CFI
CAI
AFI
AAI
CCG
0 10 20 30 40 50 60
Mosaic Mean
Percentage of images with unacceptable quality
CFI
CAI
AFI
AAI
CCG
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8
Speedup without checking overhead