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My personal attempt at creating a relatively fast iterative mergesort that runs on CUDA GPUs

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tedliosu/cuda_mergesort_ytl

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Instructions (Linux Version of This Program)

Build dependencies include;

  1. Latest complete CUDA toolkit (CUDA 12.4 as of time of writing)

  2. GNU Compiler Collection

  3. Criterion for running the unit tests

Open a terminal interface and run:

  • make run to run linear buffer version of program
  • make run_circ_buff to run circular buffer version of program (runs slower than linear version due to implementation overly-aggressively attempting to conserve memory bandwidth; Author thought this version would perform better than linear buffer version but unfortunately the circular buffer version was designed for running fast on older GPU architectures.)
  • make run_tests to run kernel unit tests for linear buffer version of program
  • make run_tests_circ_buff to run kernel unit tests for circular buffer version of program
  • make profile to run nsight compute profiling for first invocation of global_mem_mergesort_step kernel within the linear buffer version of program (nsight compute MUST be installed for this to work)
  • make profile_circ_buff to run nsight compute profiling for first invocation of global_mem_mergesort_step kernel within the circular buffer version of program (nsight compute MUST be installed for this to work)

Note: only Linux distributions are supported for now on this main branch.

TODO

  1. Add more detailed comments in at least the .cu source code files

  2. Maybe add more details in this README?

  3. Add support for sorting 64-bit integer types as compile-time feature Author deems this not important; as this is only essentially a demo program.

  4. Add unit tests at least for the CUDA kernels - Author is finding this difficult; any outside help would be appreciated; more than willing to refactor code to make unit tests easier :) Done on May 13 2024 :)

  5. Prevent people from entering too large array sizes based on max total VRAM (total VRAM - 512 mib basically). Done on May 17 2024, and didn't even have to use any special formulas :)

  6. Port over application to Windows maybe?

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My personal attempt at creating a relatively fast iterative mergesort that runs on CUDA GPUs

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