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Project 2: Dineth Meegoda #32

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Features:

  • CPU Scan & Compaction (with and without scan)
  • GPU Based Naive Scan Algorithm
  • GPU Based Work-Efficient Scan Algorithm
  • Implementation of the Thrust Library's exclusive scan
  • Performance Analysis & Benchmark

Feedback:

  • This was an interesting project, but I struggled on the profiling section. Other than noting the running times, I tried to get more information through NSight Systems and Compute but I wasn't sure how to navigate them to get the information I needed. I wanted to particularly find instances of my kernels having memory-related stalls since I did not implement shared memory, but my NSight Systems did not give me much information. I might have some of my profiling settings messed up? Now looking at other readme's, I did not have the same NSight Systems rows available as they did for the profiles which confused me. As a result, I had to make my bottleneck predictions based on my thoughts on my own code and what we've learned in class.

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