CUDA advanced Took 1hr 50 mins
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Ryan
parent
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b3a8881805
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@ -42,6 +42,7 @@
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...
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}
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```
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- Note to self: run `nvcc` with `-arch=native` otherwise it won't work.
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### Device code and Host code
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127
2-3-advanced-cuda.md
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127
2-3-advanced-cuda.md
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# Advanced CUDA Programming
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## Memory management
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- Use the following calls:
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- `cudaMalloc`: Allocate memory
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- `cudaFree`: Free memory
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- `cudaMemcpy`: Copy memory
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### Example: Addition on device
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```c
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__global__ void add(int *a, int *b, int *c) {
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// Since this runs on device, we need they to be in
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// Device memory
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*c = *a + *b;
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}
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int main(void) {
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int a, b, c;
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// host copies of a, b, c
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int *d_a, *d_b, *d_c; // device copies of a, b, c
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int size = sizeof(int);
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// Allocate space for device copies of a, b, c
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cudaMalloc((void **)&d_a, size);
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cudaMalloc((void **)&d_b, size);
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cudaMalloc((void **)&d_c, size);
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// Setup input values
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a = 2;
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b = 7;
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// Copy inputs to device
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cudaMemcpy(d_a, &a, size, cudaMemcpyHostToDevice);
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cudaMemcpy(d_b, &b, size, cudaMemcpyHostToDevice);
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// Launch add() kernel on GPU
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add<<<1, 1>>>(d_a, d_b, d_c);
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// Copy result back to host
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cudaMemcpy(&c, d_c, size, cudaMemcpyDeviceToHost);
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// Cleanup
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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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return 0;
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}
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```
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### Memory in GPU and CPU
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- They cannot dereference a pointer that points to memory not in their device:
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- Device pointers point to GPU memory, can be passed from or to host, but
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can't be dereferenced by Host
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- use APIs to manage device memory: [link](#memory-management)
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## Heterogeneous programming
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- Because both host CPU and device GPU are used
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- The code has serial code, executed by host CPU, and parallel kernel and code
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executed by the GPU
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## Parallel computing With CUDA
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### Parallel Programming
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- Kernel: executed on GPU, as an array of threads in parallel
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- Threads: execute the same code, but can take different paths
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- Thread has an ID, to select data and control decisions
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- Blocks: Threads are grouped into blocks, each block represent a stream
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processor
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- Grids: Blocks are grouped into grids
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### Iterations
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```c
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#include <stdio.h>
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__global__ void add(int *a, int *b, int *c) {
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int bid = blockIdx.x;
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printf("bid: %d\n", bid);
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if (bid < 6) { // The students can also use a "for loop here"
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c[bid] = a[bid] + b[bid];
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printf("c: %d\n", c[bid]);
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}
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}
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int main(void) {
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int a[6], b[6], c[6];
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int *dev_a, *dev_b, *dev_c;
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// allocate memory to device
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cudaMalloc((void **)&dev_a, 6 * sizeof(int));
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cudaMalloc((void **)&dev_b, 6 * sizeof(int));
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cudaMalloc((void **)&dev_c, 6 * sizeof(int));
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// Fill arrays "a" and "b" with values on the host
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for (int i = 0; i < 6; i++) {
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a[i] = i;
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b[i] = i * i;
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}
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// Copy arrays "a" and "b" to the device
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cudaMemcpy(dev_a, a, 6 * sizeof(int), cudaMemcpyHostToDevice);
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cudaMemcpy(dev_b, b, 6 * sizeof(int), cudaMemcpyHostToDevice);
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// Launch the kernel
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add<<<4, 1>>>(dev_a, dev_b, dev_c);
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// Copy the array "c" from the device to the host
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cudaMemcpy(c, dev_c, 6 * sizeof(int), cudaMemcpyDeviceToHost);
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// Print the array "c"
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for (int i = 0; i < 6; i++) {
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printf("%d\n", c[i]);
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}
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// Free memory allocated to the device
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cudaFree(dev_a);
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cudaFree(dev_b);
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cudaFree(dev_c);
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return 0;
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} // End main
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```
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### Assumptions:
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- Assume CUDA thread execute on a physically separate device than Host
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- Assume both Host and Device has their own separate memory spaces in DRAM
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### Thread vs. Block
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- Thread per block is limited, while number of block is not (they can wait until
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previous block finish)
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- Threads in a block has shared memory, while blocks don't have shared memory,
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only global memory.
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## Examples and tasks
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Binary file not shown.
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@ -1,5 +1,5 @@
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#include <stdio.h>
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#define N 10
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#define N 16
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// 1. Define the kernel
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__global__ void add(int *a, int *b, int *c) {
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int tid = blockIdx.x; // handle the data at this index
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31
assets/code/test2.cu
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31
assets/code/test2.cu
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#include <stdio.h>
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__global__ void add(int *a, int *b, int *c) {
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*c = *a + *b;
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}
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int main(void) {
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int a, b, c;
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// host copies of a, b, c
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int *d_a, *d_b, *d_c; // device copies of a, b, c
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int size = sizeof(int);
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// Allocate space for device copies of a, b, c
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cudaMalloc((void **)&d_a, size);
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cudaMalloc((void **)&d_b, size);
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cudaMalloc((void **)&d_c, size);
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// Setup input values
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a = 2;
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b = 7;
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// Copy inputs to device
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cudaMemcpy(d_a, &a, size, cudaMemcpyHostToDevice);
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cudaMemcpy(d_b, &b, size, cudaMemcpyHostToDevice);
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// Launch add() kernel on GPU
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add<<<1, 1>>>(d_a, d_b, d_c);
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// Copy result back to host
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cudaMemcpy(&c, d_c, size, cudaMemcpyDeviceToHost);
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printf("result c: %d", c);
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// Cleanup
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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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return 0;
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}
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42
assets/code/test3.cu
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42
assets/code/test3.cu
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#include <stdio.h>
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#define N 6
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__global__ void add(int *a, int *b, int *c) {
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int bid = blockIdx.x;
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printf("bid: %d\n", bid);
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if (bid < N) { // The students can also use a "for loop here"
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c[bid] = a[bid] + b[bid];
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printf("c: %d\n", c[bid]);
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}
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}
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int main(void) {
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int a[N], b[N], c[N];
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int *dev_a, *dev_b, *dev_c;
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// allocate memory to device
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cudaMalloc((void **)&dev_a, N * sizeof(int));
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cudaMalloc((void **)&dev_b, N * sizeof(int));
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cudaMalloc((void **)&dev_c, N * sizeof(int));
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// Fill arrays "a" and "b" with values on the host
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for (int i = 0; i < N; i++) {
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a[i] = i;
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b[i] = i * i;
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}
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// Copy arrays "a" and "b" to the device
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cudaMemcpy(dev_a, a, N * sizeof(int), cudaMemcpyHostToDevice);
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cudaMemcpy(dev_b, b, N * sizeof(int), cudaMemcpyHostToDevice);
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// Launch the kernel
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add<<<12, 1>>>(dev_a, dev_b, dev_c);
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// Copy the array "c" from the device to the host
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cudaMemcpy(c, dev_c, N * sizeof(int), cudaMemcpyDeviceToHost);
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// Print the array "c"
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for (int i = 0; i < N; i++) {
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printf("%d\n", c[i]);
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}
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// Free memory allocated to the device
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cudaFree(dev_a);
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cudaFree(dev_b);
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cudaFree(dev_c);
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return 0;
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} // End main
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162
mdsf.json
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162
mdsf.json
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{
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"$schema": "https://raw.githubusercontent.com/hougesen/mdsf/main/schemas/v0.3.2/mdsf.schema.json",
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"format_finished_document": false,
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"javascript_runtime": "node",
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"languages": {
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"c": "clang-format",
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"clojure": [
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[
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"cljstyle",
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"joker"
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]
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],
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"cpp": "clang-format",
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"crystal": "crystal:format",
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"csharp": [
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[
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"csharpier",
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"clang-format"
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]
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],
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"css": [
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[
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"prettier",
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"stylelint"
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]
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],
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"d": "dfmt",
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"dart": "dart:format",
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"elixir": "mix:format",
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"elm": "elm-format",
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"erlang": [
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[
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"erlfmt",
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"efmt"
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]
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],
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"gleam": "gleam:format",
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"go": [
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[
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"gci",
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"goimports-reviser",
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"goimports"
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],
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[
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"gofumpt",
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"gofmt",
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"crlfmt"
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]
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],
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"haskell": [
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[
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"fourmolu",
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"ormolu",
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"hindent",
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"stylish-haskell"
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]
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],
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"html": [
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[
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"prettier",
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"djlint"
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]
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],
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"java": [
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[
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"google-java-format",
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"clang-format"
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]
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],
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"javascript": [
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[
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"prettier",
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"biome:format",
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"deno:fmt",
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"clang-format",
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"standardjs"
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]
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],
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"json": [
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[
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"prettier",
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"biome:format",
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"deno:fmt",
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"clang-format"
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]
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],
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"kotlin": "ktfmt",
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"lua": [
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[
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"stylua",
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"luaformatter"
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]
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],
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"nim": "nimpretty",
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"ocaml": [
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[
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"ocamlformat",
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"ocp-indent"
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]
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],
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"python": [
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[
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"usort",
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"isort"
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],
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[
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"ruff:format",
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"blue",
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"black",
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"yapf",
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"autopep8",
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"pyink"
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]
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],
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"roc": "roc:format",
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"ruby": [
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[
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"rubocop",
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"rufo",
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"rubyfmt",
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"standardrb"
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]
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],
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"rust": "rustfmt",
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"shell": [
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[
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"shfmt",
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"beautysh"
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]
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],
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"sql": [
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[
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"sql-formatter",
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"sqlfluff:format",
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"sqlfmt"
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]
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],
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"swift": [
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[
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"swift-format",
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"swiftformat"
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]
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],
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"toml": "taplo",
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"typescript": [
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[
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"prettier",
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"biome:format",
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"deno:fmt"
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]
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],
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"yaml": [
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[
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"prettier",
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"yamlfmt",
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"yamlfix"
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]
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],
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"zig": "zig:fmt"
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},
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"custom_file_extensions": {}
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}
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