CPU-GPU Collaboration for Output Quality Monitoring Mehrzad Samadi and Scott Mahlke University of Michigan March 2014 Compilers creating custom processors University of Michigan Electrical 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 TOQ TOQ + delta TOQ - delta Check the quality

3 Output Quality Monitoring Sampling over time Sampling over space

4 Partial Output Quality Monitoring Accurate Version Approximate Version Evaluation Metric Subset of Input Data

5 CCG Collaborative CPU-GPU Output Quality Monitoring Approximate Run 0 GPU CPU Approximate Run 1 Approximate Run 2 Approximate Run 3 Check 1Check 2Check 3Check 4 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 Intel Core i7 CCG: Collaborative CPU-GPU approach Time Sampling Conservative Aggressive Adaptive Fixed Adaptive Fixed CFI CAI AFI AAI

7 Conservative/ Aggressive Quality TOQ TOQ + delta TOQ - delta Speedup Conservative Aggressive

8 Results

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 solution 9

CPU-GPU Collaboration for Output Quality Monitoring Mehrzad Samadi and Scott Mahlke University of Michigan March 2014 Compilers creating custom processors University of Michigan Electrical Engineering and Computer Science

11 Fixed/Adaptive Fixed Adaptive: Reduce the overhead of checking. Quality TOQ TOQ + delta TOQ - delta Quality TOQ TOQ + delta TOQ - delta

12 Results