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lukastruemperlukastruemper
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Merge pull request #1 from daisytuner/initial-examples
adds initial examples
2 parents 1aecfa7 + b1b2916 commit beec70f

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‎.daisy/c_docc.yml‎

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on:
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push:
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branches:
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- main
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pull_request:
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types: [opened, reopened, synchronize, ready_for_review]
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parameters:
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timeout: 20
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partitions:
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- bellis5
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steps:
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build: |
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docc -Xclang -no-opaque-pointers -g -O1 -mllvm -hotspot -o c/matmul.out c/matmul.c -ldaisy_rtl
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run:
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matmul:
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command: ./c/matmul.out
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measurements: 5
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profiler: perf
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loops: true
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metrics:
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- flops_dp
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- memory_bandwidth

‎.daisy/c_gcc.yml‎

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branches:
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- main
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pull_request:
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types: [opened, reopened, synchronize, ready_for_review]
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parameters:
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timeout: 20
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partitions:
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- bellis5
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steps:
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build: |
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gcc -g -O1 -o c/matmul.out c/matmul.c
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run:
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matmul:
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command: ./c/matmul.out
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measurements: 5
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profiler: perf
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metrics:
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- flops_dp
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- memory_bandwidth

‎.daisy/cuda.yml‎

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branches:
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pull_request:
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types: [opened, reopened, synchronize, ready_for_review]
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parameters:
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timeout: 20
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partitions:
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- zinnia
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steps:
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build: |
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nvcc -O1 -g -o cuda/matmul.out cuda/matmul.cu
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run:
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matmul_cu:
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command: ./cuda/matmul.out
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measurements: 5
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profiler: nsys
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kernels: true

‎.daisy/onnx.yml‎

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types: [opened, reopened, synchronize, ready_for_review]
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parameters:
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timeout: 20
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partitions:
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- tansy
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steps:
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build: |
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run:
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squeezenet:
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model: /data/squeezenet1.onnx
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measurements: 1

‎.daisy/python_apt.yml‎

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push:
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branches:
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pull_request:
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types: [opened, reopened, synchronize, ready_for_review]
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parameters:
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timeout: 20
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partitions:
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- bellis5
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steps:
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build: |
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sudo apt-get install -y python3-numpy
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run:
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matmul:
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command: python3 python/matmul_np.py
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measurements: 5
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profiler: py-spy

‎.daisy/python_conda.yml‎

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pull_request:
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types: [opened, reopened, synchronize, ready_for_review]
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parameters:
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timeout: 20
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conda: "3.12"
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partitions:
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- bellis5
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steps:
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build: |
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pip install numpy
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run:
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matmul:
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command: python python/matmul_np.py
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measurements: 5
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profiler: perf

‎.daisy/python_venv.yml‎

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branches:
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pull_request:
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types: [opened, reopened, synchronize, ready_for_review]
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parameters:
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timeout: 20
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partitions:
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- bellis5
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steps:
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build: |
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python3 -m venv venv
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. venv/bin/activate
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pip install numpy
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run:
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matmul:
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command: venv/bin/python3 python/matmul_np.py
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measurements: 5
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profiler: py-spy

‎.daisy/tflite_coral.yml‎

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branches:
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pull_request:
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types: [opened, reopened, synchronize, ready_for_review]
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parameters:
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timeout: 60
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conda: "3.8"
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partitions:
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- bellis4
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steps:
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build: |
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sudo add-apt-repository 'deb https://packages.cloud.google.com/apt coral-edgetpu-stable main'
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curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
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sudo apt-get update
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sudo apt-get install -y libedgetpu1-std
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sudo apt-get install -y usbutils
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sudo apt install -y libjpeg-dev zlib1g-dev
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wget https://github.com/google-coral/pycoral/releases/download/v2.0.0/tflite_runtime-2.5.0.post1-cp38-cp38-linux_aarch64.whl
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wget https://github.com/google-coral/pycoral/releases/download/v2.0.0/pycoral-2.0.0-cp38-cp38-linux_aarch64.whl
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pip install Pillow==9.5.0
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pip install tflite_runtime-2.5.0.post1-cp38-cp38-linux_aarch64.whl
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pip install pycoral-2.0.0-cp38-cp38-linux_aarch64.whl
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git clone --recurse-submodules --branch v2.0.0 --depth 1 https://github.com/google-coral/pycoral
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cd pycoral
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bash examples/install_requirements.sh classify_image.py
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run:
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list_usb:
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command: lsusb | grep Google
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classify_image:
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command: python pycoral/examples/classify_image.py --model pycoral/test_data/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite --labels pycoral/test_data/inat_bird_labels.txt --input pycoral/test_data/parrot.jpg

‎c/matmul.c‎

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#include <stdio.h>
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#define N 64
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#define M 64
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#define K 64
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int main() {
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int A[N][K];
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int B[K][M];
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int C[N][M];
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// Init
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for (int i = 0; i < N; i++) {
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for (int j = 0; j < K; j++) {
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A[i][j] = i + j;
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}
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}
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for (int i = 0; i < K; i++) {
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for (int j = 0; j < M; j++) {
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B[i][j] = i + j;
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}
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}
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for (int i = 0; i < N; i++) {
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for (int j = 0; j < M; j++) {
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C[i][j] = 0;
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}
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}
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// Matmul
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for (int i = 0; i < N; i++) {
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for (int j = 0; j < M; j++) {
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for (int k = 0; k < K; k++) {
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C[i][j] += A[i][k] * B[k][j];
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}
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}
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}
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// Print
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for (int i = 0; i < N; i++) {
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for (int j = 0; j < M; j++) {
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printf("%d ", C[i][j]);
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}
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printf("\n");
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}
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return 0;
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}

‎cuda/matmul.cu‎

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#include <cuda_runtime.h>
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#include <stdio.h>
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#include <stdlib.h>
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#define TILE_SIZE 16
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inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort = true) {
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if (code != cudaSuccess) {
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fprintf(stderr, "CUDA error: %s %s %d\n", cudaGetErrorString(code), file, line);
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if (abort) exit(code);
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}
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}
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__global__ void matrixMulKernel(float *A, float *B, float *C, int N) {
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__shared__ float sharedA[TILE_SIZE][TILE_SIZE];
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__shared__ float sharedB[TILE_SIZE][TILE_SIZE];
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int tx = threadIdx.x, ty = threadIdx.y;
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int row = blockIdx.y * TILE_SIZE + ty;
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int col = blockIdx.x * TILE_SIZE + tx;
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float sum = 0.0f;
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for (int t = 0; t < (N + TILE_SIZE - 1) / TILE_SIZE; t++) {
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if (row < N && t * TILE_SIZE + tx < N)
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sharedA[ty][tx] = A[row * N + t * TILE_SIZE + tx];
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else
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sharedA[ty][tx] = 0.0f;
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if (col < N && t * TILE_SIZE + ty < N)
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sharedB[ty][tx] = B[(t * TILE_SIZE + ty) * N + col];
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else
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sharedB[ty][tx] = 0.0f;
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__syncthreads();
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for (int k = 0; k < TILE_SIZE; k++) {
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sum += sharedA[ty][k] * sharedB[k][tx];
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}
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__syncthreads();
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}
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if (row < N && col < N)
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C[row * N + col] = sum;
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}
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void matrixMultiply(float *h_A, float *h_B, float *h_C, int N) {
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float *d_A, *d_B, *d_C;
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size_t size = N * N * sizeof(float);
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cudaMalloc(&d_A, size);
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cudaMalloc(&d_B, size);
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cudaMalloc(&d_C, size);
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cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
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cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);
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dim3 blockSize(TILE_SIZE, TILE_SIZE);
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dim3 gridSize((N + TILE_SIZE - 1) / TILE_SIZE, (N + TILE_SIZE - 1) / TILE_SIZE);
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matrixMulKernel<<<gridSize, blockSize>>>(d_A, d_B, d_C, N);
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cudaDeviceSynchronize();
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gpuAssert(cudaGetLastError(), __FILE__, __LINE__);
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cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);
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cudaFree(d_A);
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cudaFree(d_B);
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cudaFree(d_C);
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}
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int main() {
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int N = 64; // Matrix size
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size_t size = N * N * sizeof(float);
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float *h_A = (float *)malloc(size);
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float *h_B = (float *)malloc(size);
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float *h_C = (float *)malloc(size);
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for (int i = 0; i < N * N; i++) {
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h_A[i] = rand() % 10;
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h_B[i] = rand() % 10;
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}
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matrixMultiply(h_A, h_B, h_C, N);
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printf("Result matrix:\n");
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for (int i = 0; i < N; i++) {
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for (int j = 0; j < N; j++) {
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printf("%0.1f ", h_C[i * N + j]);
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}
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printf("\n");
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}
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free(h_A);
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free(h_B);
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free(h_C);
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return 0;
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}

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