[rocm-libraries] ROCm/rocm-libraries#4272 (commit 52def72)

feat: add new optimized tutorial kernels
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- Add 01_naive_gemm baseline implementation
- Add 02_padding_k_first with PADDING_K_FIRST + MFMA_32x32x16
- Add 03_mfma_16x16x16 with PADDING_K_FIRST + MFMA_16x16x16
- Share common reference_gemm.hpp in parent gemm/ directory

## Proposed changes

Please describe the motivation behind the pull request, whether it
enables a new feature or fixes a bug. If there are associated pull
requests or issues, please link them to the pull request.

## Checklist

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after creating the PR. If you're not sure, please don't hesitate to ask.

- [ ] I have added tests relevant to the introduced functionality, and
the unit tests are passing locally
- [ ] I have added the test to REGRESSION_TESTS list defined at the top
of CMakeLists.txt in tests/CMakeLists.txt, **IF** the test takes more
than 30 seconds to run.
- [ ] I have added inline documentation which enables the maintainers
with understanding the motivation
- [ ] I have removed the stale documentation which is no longer relevant
after this pull request
- [ ] (If this change is user-facing) I have added release notes which
provide the end users with a brief summary of the improvement from this
pull request
- [ ] I have run `clang-format` on all changed files
- [ ] Any dependent changes have been merged

## Discussion

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discussion by explaining why you chose the solution you did and what
alternatives you considered

tutorial/ck_tile/gemm/reference_gemm.hpp

@@ -0,0 +1,37 @@
+// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
+// SPDX-License-Identifier: MIT
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/host/host_tensor.hpp"
+
+template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
+void reference_basic_gemm(const ck_tile::HostTensor<ADataType>& a_m_k,
+                          const ck_tile::HostTensor<BDataType>& b_n_k,
+                          ck_tile::HostTensor<CDataType>& c_m_n)
+{
+    const int N = b_n_k.mDesc.get_lengths()[0];
+    const int K = b_n_k.mDesc.get_lengths()[1];
+
+    auto f = [&](auto m) {
+        for(int n = 0; n < N; ++n)
+        {
+            AccDataType v_acc = 0;
+
+            for(int k = 0; k < K; ++k)
+            {
+                ADataType v_a = a_m_k(m, k);
+                BDataType v_b = b_n_k(n, k);
+
+                v_acc += ck_tile::type_convert<AccDataType>(v_a) *
+                         ck_tile::type_convert<AccDataType>(v_b);
+            }
+
+            c_m_n(m, n) = ck_tile::type_convert<CDataType>(v_acc);
+        }
+    };
+
+    ck_tile::make_ParallelTensorFunctor(f, c_m_n.mDesc.get_lengths()[0])(
+        std::thread::hardware_concurrency());
+}