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ACENET Summer School - GPGPU

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Putting It All Together

Overview

Teaching: 10 min
Exercises: 20 min
Questions

  • How do blocks and threads work together?

  • What other GPU programming resources are there?

Objectives

  • To show how blocks and threads typically work together

To take advantage of all these “CUDA cores” you need to use both blocks and threads.

The family of CUDA variables defining blocks and threads can be explained by referring to this image from “An Even Easier Introduction to CUDA”:

CUDA indexing

The number of blocks is in gridDim.x— we’ve been calling that numBlocks in our CPU-side code— and the number of threads in a block is blockDim.x which we’ve been calling numThreads. CUDA also provides the index of the current block in blockIdx.x and the thread index within that block is threadIdx.x, both of which we saw earlier.

All the indexing is zero-based, like in C.

If you’re wondering about the .x on all of these, it’s there because you have the option of specifying 2- or 3-dimensional arrays of blocks, and threads within blocks, for natural indexing of 2- and 3-dimensional data structures likes matrices or volumes. See the CUDA Programming Guide if you want to learn more about this.

We’ll change our kernel function one more time, to escape the limitation of the thread count and get (we hope) maximal performance. We’ll also put a loop back in, in case the size of our data is greater than the number of (blocks times threads) we have:

__global__ void add(int n, int *a, int *b, int *c) {
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   int stride = blockDim.x * gridDim.x;
   for (int i = index; i < n; i += stride)
      c[i] = a[i] + b[i];
}

Now the size of dataset (i.e. the length of the vectors) we can handle correctly no longer depends on the number of blocks and threads.

We still need to choose a number of blocks and a thread count.

We’ve seen that threads-per-block can’t go higher than 1024, and 256 is a fairly conventional choice. We could choose a number of blocks that exactly covers the size of our arrays by dividing N / numthreads, and adding an extra block to handle any remainder from the division. But by introducing a loop into the kernel we just wrote, it should be able to handle any choice of block and thread count that we give it. So now we’re free to play with numBlocks and numThreads all we like, checking performance with nvprof to see how much difference various choices make.

Putting it all together

Introduce the add() kernel above into your code from the previous episodes, and adjust the rest of the program to make it work.

  • Verify that it still produces correct results.
  • Use nvprof to compare the performance to that of the two previous solutions.

Solution

Here’s a version of the code that includes a few more “bells and whistles”. Most importantly, you need to specify the addends, the size of the arrays, and the number of threads and blocks. This makes it easier to explore the correctness and performance of these choices.

#include <stdio.h> 
#include <stdlib.h>
 
__global__ void add(int N, int *da, int *db, int *dc) {
   // This is a CUDA idiom called the grid-stride loop.
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   int stride = blockDim.x * gridDim.x;
   for (int i = index; i < N; i += stride)
      dc[i] = da[i] + db[i];
}

int main(int argc, char **argv) {
   // Read values from cmd line.
   if (argc < 6) {
      printf("Usage:\n %s a b N threads blocks\n", argv[0]);
      return(-1);
   }
   int a_in = atoi(argv[1]);
   int b_in = atoi(argv[2]);
   int N = atoi(argv[3]);
   int numThreads = atoi(argv[4]);
   int numBlocks = atoi(argv[5]);
   // Or to get the block count that covers N elements:
   // int numBlocks = (N + numThreads - 1) / numThreads;

   // Calculate size of arrays in bytes.
   int size = N * sizeof(int);
   // Allocate host storage.
   int *a, *b, *c;
   a = (int *)malloc(size);
   b = (int *)malloc(size);
   c = (int *)malloc(size);
   // Initialize the input vectors.
   for (int i=0; i<N; ++i) {
      a[i] = a_in; 
      b[i] = b_in;
      c[i] = 0;
   }

   // Allocate device storage.
   int *da, *db, *dc;
   cudaMalloc((void **)&da, size);
   cudaMalloc((void **)&db, size);
   cudaMalloc((void **)&dc, size);

   // Copy data to GPU.
   cudaMemcpy(da, a, size, cudaMemcpyHostToDevice);
   cudaMemcpy(db, b, size, cudaMemcpyHostToDevice);

   // Execute the kernel on the GPU.
   add<<<numBlocks,numThreads>>>(N, da, db, dc);
   cudaDeviceSynchronize();

   // Copy results back from GPU.
   cudaMemcpy(c, dc, size, cudaMemcpyDeviceToHost);
   
   // Print results from each end of the array.
   printf("%d plus %d equals %d\n", a[0], b[0], c[0]);
   printf(" ...\n");
   printf("%d plus %d equals %d\n", a[N-1], b[N-1], c[N-1]);

   // Check for stray errors somewhere in the middle.
   // We won't check them all, quit after first error.
   int expected = a_in + b_in;
   for (int i=0; i<N; ++i) {
      if (c[i] != expected) {
         printf("Wrong sum %d at element %d!\n", c[i], i);
	 break;
      }
   }

   // Free all allocated memory.
   cudaFree(da); cudaFree(db); cudaFree(dc);
   free(a); free(b); free(c);
}

Compile it and invoke it like this:

$ nvcc addvec_final.cu -o final
$ srun --gres=gpu:1 final 2 2 10000 256 80

Remember, you can also use salloc in place of srun to get a shell prompt on a GPU node so you can more easily run multiple trials, and using nvprof final ... will give you performance information. (Except on our virtual cluster. Sorry!)

Other bits and pieces

You can define shared variables in your kernel functions that are visible to all running threads in a block. Maybe you want to have a flag to record whether some unusual condition arose while processing a block of data?

__global__ void add(int *a, int *b, int *c) {
   __shared__ int block_status;
   c[threadIdx.x] = a[threadIdx.x] + b[threadIdx.x];
}

You can also synchronize the execution of code by setting barriers to get threads to reach common points in the problem. Let’s say that you are evolving some system and need all of the threads to finish their work before going onto the next step. This is where synchronization comes into play.

# Do the current time step
__syncthreads();
# Go on to do the next time step

This helps avoid problems like race conditions, where incorrect data is being used in your calculation. GPU programming is basically a type of shared-memory programming, so the same problems and cautions we saw with OpenMP apply here.

Where to go next

This has been the barest of introductions to CUDA and GPU programming. Don’t forget the CUDA Programming Guide we mentioned earlier:

As mentioned in episode 1, there are other ways to program GPUs than CUDA. Here are two OpenACC tutorials, including one from the Alliance:

If you’re using GPUs, then performance obviously matters to you. A lot. Here’s a great blog post that summarizes the most important performance issues around GPUs. It’s from a perspective of Deep Learning, but the thoughts are quite general:

Key Points

  • A typical kernel indexes data using both blocks and threads

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