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[ck] Enforce LF-only line endings in C/C++ sources
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## Summary
Several CK source files carry Windows **CRLF** line endings (a trailing
carriage return on each line), introduced by editors configured for
Windows endings or copy/paste from Windows tooling. These are purely
cosmetic but they pollute diffs (whole-file churn the first time someone
makes an LF edit), confuse `clang-format`, and are inconsistent with the
LF-only convention used across the rest of the tree.
This PR (a) normalizes every existing CRLF file (6 files) to LF and (b)
adds a pre-checkin gate so new CRLF leaks are rejected before merge.
## File extensions covered
Both the cleanup scan and the new Jenkins enforcement stage use the same
predicate as the adjacent `ASCII Only Check` stage:
```
*.h *.hpp *.cpp *.h.in *.hpp.in *.cpp.in *.inc *.cl
```
(excluding `*/build/*` and `*/include/rapidjson/*`). The local
pre-commit hook's `c++/inc` type filter covers the same set.
## Why no enforcement today
CK is opted out of the rocm-libraries root `.pre-commit-config.yaml`, so
the existing `pre-commit` workflow doesn't touch CK. The local CK
`.pre-commit-config.yaml` only runs for developers who installed hooks.
The **authoritative gate is therefore the new Jenkins stage** in this
PR; the local hook is convenience.
## Commit layout (bisect-friendly)
1. `[ck] Normalize CRLF line endings to LF in C/C++ sources`
Mechanical line-ending cleanup across 6 files. No content change: every
edit is purely CRLF -> LF, verified with `git diff --ignore-cr-at-eol`
reporting an empty diff.
2. `[ck] Enforce LF-only line endings in C/C++ sources`
- New `projects/composablekernel/script/check_no_crlf.sh` (modeled on
`check_ascii_only.sh`).
- New `crlf-checker` entry in
`projects/composablekernel/.pre-commit-config.yaml` under the
local-hooks block (`types_or: [c++, inc]`).
- New `CRLF Check` parallel stage in
`projects/composablekernel/Jenkinsfile`'s `Static checks` block,
mirroring the adjacent `ASCII Only Check` stage. Always-on, no
`RUN_CPPCHECK` gate.
The tree is buildable at every commit boundary. Commit 1 leaves 0 CRLF
violations; commit 2 wires the gate.
## Demo
Script output on a synthesized violation:
```
$ printf 'int main() {}\r\n' > /tmp/bad.cpp
$ projects/composablekernel/script/check_no_crlf.sh /tmp/bad.cpp
ERROR: /tmp/bad.cpp contains CRLF (Windows) line endings:
1:int main() {}<CR>
Fix: convert to LF, e.g. 'sed -i 's/\r$//' /tmp/bad.cpp' or 'dos2unix /tmp/bad.cpp'
$ echo $?
1
```
Full repo scan after the cleanup commit:
```
$ cd projects/composablekernel && find . -type f \( -name '*.h' -o -name '*.hpp' -o -name '*.cpp' \
-o -name '*.h.in' -o -name '*.hpp.in' -o -name '*.cpp.in' -o -name '*.inc' -o -name '*.cl' \) \
-not -path '*/build/*' -not -path '*/include/rapidjson/*' -print0 \
| xargs -0 -P 8 -n 64 script/check_no_crlf.sh
$ echo $?
0
```
## Test plan
- [ ] Jenkins PR build: confirm new `Static checks -> CRLF Check` stage
runs green over the full predicate and the existing `ASCII Only Check` /
`Clang Format` stages are unaffected.
- [ ] Local: `pre-commit run crlf-checker --all-files` runs cleanly
after installing CK pre-commit hooks.
- [ ] Manually inject a CRLF line ending in any `.cpp/.hpp/.inc` file,
push: confirm Jenkins fails the new stage with a clear error.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
277 lines
9.1 KiB
C++
277 lines
9.1 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#include "ck/utility/common_header.hpp"
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#include "ck/ck.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/host_utility/hip_check_error.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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#include "ck/host_utility/flush_cache.hpp"
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#include <hip/hip_runtime.h>
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#include "ck/utility/data_cache_prefetch.hpp"
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namespace ck {
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namespace prefetch_op_util {
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template <typename T>
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struct KernelArgs
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{
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const T* p_a_grid;
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T* dst;
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const T* p_b_grid;
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bool enable_prefetch;
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};
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template <typename T, uint32_t NUM_THREADS, uint32_t NUM_SCALARS, typename PrefetchOp>
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__global__ void kernel_with_prefetch(KernelArgs<T> args)
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{
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const T* src = args.p_a_grid;
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T* dst = args.dst;
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const T* scalar_data = args.p_b_grid;
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bool enable_prefetch = args.enable_prefetch;
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uint32_t tid = blockIdx.x * blockDim.x + threadIdx.x;
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// Calculate number of 32B cachelines needed to cover num_scalars elements
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constexpr index_t cachelineSize = 32;
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constexpr index_t elements_per_cachelineSize = cachelineSize / sizeof(T);
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constexpr unsigned int cachelinesNeeded =
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(NUM_SCALARS + elements_per_cachelineSize - 1) / elements_per_cachelineSize;
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const char* byte_addr = reinterpret_cast<const char*>(scalar_data);
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// Prefetch all scalar data at once
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if(tid < cachelinesNeeded)
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{
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if(enable_prefetch)
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{
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// Prefetch the cacheline
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PrefetchOp{}(byte_addr + tid * cachelineSize);
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}
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}
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T sum = 0;
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if(tid < NUM_THREADS)
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{
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sum = src[tid]; // load from global mem to give time for prefetch to finish or be close to
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// finish
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}
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__syncthreads(); // waits on loads from global mem
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if(tid < NUM_THREADS)
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{
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// Access prefetched scalar data
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for(uint32_t i = 0; i < NUM_SCALARS; i++)
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{
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sum += scalar_data[i]; // should be fast due to scalars being preloaded
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}
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dst[tid] = sum;
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}
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}
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template <typename T, uint32_t NUM_THREADS, uint32_t NUM_SCALARS, typename PrefetchOp>
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__global__ void kernel_with_prefetch_and_shared_mem(KernelArgs<T> args)
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{
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const T* src = args.p_a_grid;
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T* dst = args.dst;
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const T* scalar_data = args.p_b_grid;
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bool enable_prefetch = args.enable_prefetch;
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__shared__ T sharedMem[32];
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uint32_t tid = blockIdx.x * blockDim.x + threadIdx.x;
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// Calculate number of 32B cachelines needed to cover num_scalars elements
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constexpr index_t cachelineSize = 32;
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constexpr index_t elements_per_cachelineSize = cachelineSize / sizeof(T);
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constexpr unsigned int cachelinesNeeded =
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(NUM_SCALARS + elements_per_cachelineSize - 1) / elements_per_cachelineSize;
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bool use_shared_mem = tid % 2 == 1;
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const void* byte_addr;
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if(use_shared_mem)
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{
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byte_addr = reinterpret_cast<const void*>(sharedMem);
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}
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else
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{
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uintptr_t base = reinterpret_cast<uintptr_t>(scalar_data);
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uintptr_t offset = base + (tid / 2) * cachelineSize;
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byte_addr = reinterpret_cast<const void*>(offset);
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}
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// Prefetch all scalar data at once
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if(tid < cachelinesNeeded * 2)
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{
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if(enable_prefetch)
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{
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// Prefetch the cacheline
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PrefetchOp{}(byte_addr);
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}
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else
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{
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(void)byte_addr;
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}
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}
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T sum = 0;
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if(tid < NUM_THREADS)
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{
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sum = src[tid]; // load from global mem to give time for prefetch to finish or be close to
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// finish
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}
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__syncthreads(); // waits on loads from global mem
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if(tid < NUM_THREADS)
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{
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// Access prefetched scalar data
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for(uint32_t i = 0; i < NUM_SCALARS; i++)
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{
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sum += scalar_data[i]; // should be fast due to scalars being preloaded
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}
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dst[tid] = sum;
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}
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}
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template <typename PrefetchKernel, typename T, uint32_t NUM_THREADS, uint32_t NUM_SCALARS>
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bool test_prefetch_impl(bool time_kernels,
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const PrefetchKernel& prefetch_kernel,
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const std::string& kernel_name)
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{
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constexpr index_t block_size = 256;
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constexpr index_t num_elements = NUM_THREADS;
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constexpr index_t num_scalars = NUM_SCALARS;
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// TODO: maybe add more prefetch instructions inside kernel to support more values
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assert(NUM_SCALARS / sizeof(T) < (32 * block_size) &&
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"Too many scalars to prefetch with current implementation!");
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constexpr index_t grid_size = (num_elements + block_size - 1) / block_size;
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std::cout << "Testing " << kernel_name << " for type: " << typeid(T).name() << std::endl;
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std::cout << "Elements: " << num_elements << ", Scalars: " << num_scalars << std::endl;
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// Host data
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std::vector<T> h_src(num_elements);
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std::vector<T> h_scalar(num_scalars);
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std::vector<T> h_dst_with_prefetch_chunks(num_elements);
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std::vector<T> h_expected(num_elements);
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// Initialize data
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for(index_t i = 0; i < num_elements; i++)
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{
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h_src[i] = static_cast<T>(i % 100);
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}
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T scalar_sum = 0;
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for(index_t i = 0; i < num_scalars; i++)
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{
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h_scalar[i] = static_cast<T>(i + 1);
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scalar_sum += h_scalar[i];
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}
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// Expected results
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for(index_t i = 0; i < num_elements; i++)
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{
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h_expected[i] = h_src[i] + scalar_sum;
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}
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// Device memory
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DeviceMem d_src(sizeof(T) * num_elements);
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DeviceMem d_scalar(sizeof(T) * num_scalars);
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DeviceMem d_dst_with_prefetch_chunks(sizeof(T) * num_elements);
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d_src.ToDevice(h_src.data());
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d_scalar.ToDevice(h_scalar.data());
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KernelArgs<T> args{static_cast<const T*>(d_src.GetDeviceBuffer()),
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static_cast<T*>(d_dst_with_prefetch_chunks.GetDeviceBuffer()),
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static_cast<const T*>(d_scalar.GetDeviceBuffer()),
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true};
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if(time_kernels)
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{
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std::array<float, 2> avg_times_us;
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ck::static_for<0, 2, 1>{}([&](auto static_i) {
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constexpr bool prefetch_enabled = static_i == 0;
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std::cout << "PREFETCH " << (prefetch_enabled ? "ENABLED!" : "DISABLED!") << std::endl;
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args.enable_prefetch = prefetch_enabled;
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constexpr int num_warmup = 1;
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constexpr int num_iterations = 10;
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constexpr int rotating_count = num_iterations;
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auto size_a_buffer = d_src.GetBufferSize();
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auto size_b_buffer = d_scalar.GetBufferSize();
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ck::utility::RotatingMemWrapper<KernelArgs<T>> rotating_mem(
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args, rotating_count, size_a_buffer, size_b_buffer);
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rotating_mem.Print();
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auto run_flush_cache = [&]() {
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// flush icache
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ck::utility::flush_icache();
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// rotating mem
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rotating_mem.Next();
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};
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float avg_time_ms = ck::utility::launch_and_time_kernel_with_preprocess<false>(
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StreamConfig{nullptr, true, 0, num_warmup, num_iterations, true, rotating_count},
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run_flush_cache,
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prefetch_kernel,
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dim3(grid_size),
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dim3(block_size),
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0,
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args);
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float avg_time_us = avg_time_ms * 1000.0f;
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float total_bytes = (size_a_buffer + size_b_buffer); // read
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float bandwidth_gb_s = (total_bytes / (avg_time_us * 1e-6)) / 1e9;
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float ops_per_iteration = num_elements * num_scalars; // adds
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float gflops = (ops_per_iteration / (avg_time_us * 1e-6)) / 1e9;
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std::cout << " Performance: " << std::endl;
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std::cout << " Average kernel time: " << avg_time_us << " us" << std::endl;
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std::cout << " Effective bandwidth: " << bandwidth_gb_s << " GB/s" << std::endl;
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std::cout << " Compute throughput: " << gflops << " GFLOPS" << std::endl;
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avg_times_us[static_i] = avg_time_us;
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});
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float speedup = avg_times_us[1] / avg_times_us[0];
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std::cout << "On average kernel with prefetch is " << speedup
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<< " times faster than without prefetch." << std::endl;
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if(speedup < 1.0f)
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std::cout << "WARNING: prefetch kernel is slower!" << std::endl;
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}
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else
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{
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launch_and_time_kernel(StreamConfig{nullptr, false},
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prefetch_kernel,
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dim3(grid_size),
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dim3(block_size),
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0, // lds_byte
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args);
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}
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// Copy results back
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d_dst_with_prefetch_chunks.FromDevice(h_dst_with_prefetch_chunks.data());
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// Verify results
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bool pass = ck::utils::check_err(h_dst_with_prefetch_chunks, h_expected);
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std::cout << " Correctness: " << (pass ? "PASS" : "FAIL") << std::endl;
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std::cout << std::endl;
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return pass;
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}
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} // namespace prefetch_op_util
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} // namespace ck
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