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doc: code gen task 1
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.github/workflows/benchmarks.yml

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.github/workflows/ci.yml

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pull_request:
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branches: [ "main" ]
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env:
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# Customize the CMake build type here (Release, Debug, RelWithDebInfo, etc.)
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BUILD_TYPE: Release
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jobs:
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build:
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runs-on: ubuntu-24.04-arm
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strategy:
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matrix:
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build_type: ["Release", "Debug"]
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steps:
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- uses: actions/checkout@v4
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# Choose CMakeLists.text from a specific source directory with -S.
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# Configure CMake in a 'build' subdirectory. `CMAKE_BUILD_TYPE` is only required if you are using a single-configuration generator such as make.
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# See https://cmake.org/cmake/help/latest/variable/CMAKE_BUILD_TYPE.html?highlight=cmake_build_type
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run: cmake -S ${{github.workspace}}/submissions/submission_25_05_08 -B ${{github.workspace}}/build -DCMAKE_BUILD_TYPE=${{env.BUILD_TYPE}}
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run: |
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cmake -S ${{github.workspace}}/submissions/submission_25_05_01 -B ${{github.workspace}}/build/submission_25_05_01 -DCMAKE_BUILD_TYPE=${{matrix.build_type}}
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cmake -S ${{github.workspace}}/submissions/submission_25_05_08 -B ${{github.workspace}}/build/submission_25_05_08 -DCMAKE_BUILD_TYPE=${{matrix.build_type}}
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cmake -S ${{github.workspace}} -B ${{github.workspace}}/build -DCMAKE_BUILD_TYPE=${{matrix.build_type}}
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- name: Build
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# Build your program with the given configuration
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run: cmake --build ${{github.workspace}}/build --config ${{env.BUILD_TYPE}}
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run: |
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cmake --build ${{github.workspace}}/build/submission_25_05_01 --config ${{matrix.build_type}}
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cmake --build ${{github.workspace}}/build/submission_25_05_08 --config ${{matrix.build_type}}
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cmake --build ${{github.workspace}}/build --config ${{matrix.build_type}}
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- name: Test
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working-directory: ${{github.workspace}}/build
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# Execute tests defined by the CMake configuration.
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run: ctest -C ${{env.BUILD_TYPE}}
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run: |
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ctest -C ${{matrix.build_type}} --test-dir submission_25_05_01
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ctest -C ${{matrix.build_type}} --test-dir submission_25_05_08
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ctest -C ${{matrix.build_type}}
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docs_sphinx/api/mini_jit.rst

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.. _api_mini_jit:
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mini_jit
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========
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.. doxygennamespace:: mini_jit
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:project: Machine Learning Compilers
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:content-only:
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:members:
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:private-members:
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:undoc-members:
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arm_instructions
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----------------
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.. doxygennamespace:: mini_jit::arm_instructions
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:project: Machine Learning Compilers
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:content-only:
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:members:
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:private-members:
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:undoc-members:
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internal
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^^^^^^^^
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.. doxygennamespace:: mini_jit::arm_instructions::internal
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:project: Machine Learning Compilers
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:content-only:
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:members:
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:private-members:
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:undoc-members:

docs_sphinx/api/mlc.rst

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docs_sphinx/index.rst

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submissions/report_25_04_17.rst
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submissions/report_25_04_24.rst
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submissions/report_25_05_01.rst
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submissions/report_25_05_08.rst
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.. toctree::
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:maxdepth: 4

docs_sphinx/submissions/report_25_05_01.rst

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- File ``submissions/submission_25_05_01/neon_3_1.s``
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.. code-block:: asm
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:linenos:
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:linenos:
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...
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// Offset the used leading dimension by the size of floats
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- File ``submissions/submission_25_05_01/neon_3_2.s``
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.. code-block:: asm
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:linenos:
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:linenos:
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// Offset the used leading dimension by the size of floats
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lsl x3, x3, #2 // x3 * 4 = x3 * sizeof(float)
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matmul_loop_over_M:
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sub x16, x16, #1
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... <logic of loop over K - neon_3_1>
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// ... <logic of loop over K - neon_3_1>
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// next M iteration on the matrix c and matrix a, both need offset about 16 values
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// also matrix b needs to start at the initial location again
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// Loop back to M
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cbnz x16, matmul_loop_over_M
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3. Loop over N: Implement a kernel that computes C+=AB for M=64, N=48 and K=64.
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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- File ``submissions/submission_25_05_01/neon_3_3.s``
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.. code-block:: asm
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:linenos:
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:linenos:
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// Offset the used leading dimension by the size of floats
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lsl x3, x3, #2 // x3 * 4 = x3 * sizeof(float)
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matmul_loop_over_N:
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sub x17, x17, #1
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... <logic of loop over M - neon_3_2>
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// ... <logic of loop over M - neon_3_2>
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// next M iteration on the matrix b and matrix c, both need offset about 6*ldb/ldc values
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// also matrix a needs to start at the initial location again
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// Loop back to N
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cbnz x17, matmul_loop_over_N
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4. Test and optimize the kernels. Report your performance in GFLOPS.
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1. Test and optimize the kernels. Report your performance in GFLOPS.
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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- File ``submissions/submission_25_05_01/neon_3.h``
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- Tests ``submissions/submission_25_05_01/neon_3.test.cpp``

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