CPU-GPU Collaboration for Output Quality Monitoring
This study explores the collaborative approach of CPU and GPU for output quality monitoring, highlighting the challenges and solutions for different types of applications. It discusses sampling over time and space, partial monitoring, evaluation of image processing applications, and the balance between aggressive and conservative quality monitoring strategies.
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Presentation Transcript
CPU-GPU Collaboration for Output Quality Monitoring Mehrzad Samadi and Scott Mahlke University of Michigan March 2014 University of Michigan Compilers creating custom processors Electrical Engineering and Computer Science
Output Quality Monitoring Sampling over time Green[PLDI2010], SAGE[MICRO2013] Quality TOQ + delta TOQ TOQ - delta Check the quality Works fine for applications with temporal similarity for example video processing What about applications without temporal similarity? 2
Output Quality Monitoring Sampling over time Sampling over space 3
Partial Output Quality Monitoring Subset of Input Data Evaluation Metric Accurate Version Approximate Version 4
CCG Collaborative CPU-GPU Output Quality Monitoring Approximate Run 0 Approximate Run 1 Approximate Run 2 Approximate Run 3 GPU Decision Decision Decision CPU Check 1 Check 2 Check 3 Check 4 CPU performs the monitoring while GPU is executing the approximate code 5
Evaluation Two Image processing applications: Mosaic Mean Filter 1600 flower images NVIDIA GTX 560 + Intel Core i7 CCG: Collaborative CPU-GPU approach Adaptive Fixed Aggressive AAI AFI Time Sampling Adaptive Fixed Conservative CAI CFI 6
Conservative/ Aggressive Quality TOQ + delta TOQ TOQ - delta Aggressive Speedup Conservative 7
Results Mosaic Mean CCG AAI AFI CAI CFI 0 10 20 30 40 50 60 Percentage of images with unacceptable quality CCG AAI AFI CAI CFI 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 Speedup 8
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 solution 9 9
CPU-GPU Collaboration for Output Quality Monitoring Mehrzad Samadi and Scott Mahlke University of Michigan March 2014 University of Michigan Compilers creating custom processors Electrical Engineering and Computer Science
Fixed/Adaptive Fixed Quality TOQ + delta TOQ TOQ - delta Adaptive: Reduce the overhead of checking. Quality TOQ + delta TOQ TOQ - delta 11
Results Mosaic Mean CCG AAI AFI CAI CFI 0 10 20 30 40 50 60 Percentage of images with unacceptable quality CCG AAI AFI CAI CFI 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 Speedup without checking overhead 12
