CUDA threads and blocks

introduction

In this article, the basic concepts and techniques of CUDA thread and memory management will be introduced. These are necessary knowledge to learn CUDA programming in depth, because thread and memory management are the core of GPU parallel computing.

1. Threads and thread blocks

1.1 The concept of thread and block

At the heart of the CUDA programming model are threads and thread blocks. A thread is the basic unit for performing parallel computing, and a thread block is a collection containing a group of threads. A block can contain multiple threads, each of which can perform a different computational task.

A CUDA program can start multiple thread blocks, and each thread block has a unique identifier called a block index. Each thread block can contain multiple threads, and each thread has a unique identifier called a thread index.

In CUDA, the number of threads and blocks is controlled by the programmer. Programmers can adjust the number of threads and blocks according to hardware constraints and the needs of computing tasks. If the number of threads and blocks is set too low, it may cause computing tasks not to take full advantage of the parallel performance of the GPU. If set too much, it may cause starvation of GPU resources.

1.2 Parallel Execution of Threads and Blocks

In CUDA, threads and blocks can execute in parallel. A CUDA program can start multiple thread blocks, and the threads in each thread block can execute in parallel. During execution, threads and blocks can cooperate for efficient parallel computing.

In CUDA, a thread is uniquely identified by a thread ID (Thread ID), and a thread block is uniquely identified by a block ID (Block ID). Both thread ID and block ID are three-dimensional vectors, which can be expressed as (x, y, z). The size of a thread block can also be expressed in the form of a three-dimensional vector (x, y, z), that is, it contains x * y * z threads. For example, in a CUDA program, a programmer can use the following code to access thread and block indices:

int blockId = blockIdx.x;
int threadId = threadIdx.x;

In this example, blockIdx.x and threadIdx.x represent block and thread indices, respectively. Programmers can use these variables to control the behavior of threads and blocks as needed.

1.3 How to use threads and blocks in CUDA programs

In CUDA programming, the number and size of thread blocks and threads need to be explicitly specified. This can be achieved by specifying the grid and block dimensions when launching the kernel. For example, you can use the following statement to start a thread block containing 16 * 16 threads:

my_kernel<<<1, dim3(16, 16)>>>(...);

In this example, 1 means that the grid contains a thread block, and dim3(16, 16) means that the size of the thread block is 16 * 16.

2. Thread synchronization

In parallel computing, synchronization and communication are required between threads to ensure the correctness and consistency of parallel computing. CUDA provides several mechanisms for thread synchronization and communication, including mutexes, semaphores, and barriers.

2.1 Mutex

Mutex (Mutex) is a common thread synchronization mechanism used to protect critical section code to prevent multiple threads from accessing the same resource at the same time. CUDA provides a hardware-based implementation of mutexes called "CUDA Locks". The general steps for using CUDA locks are as follows:

1. Declare a variable of type int in the global memory as a lock.
2. Use this function to set the lock to "1" by calling the atomicExch() function before the critical section code to be protected.
3. Call the atomicExch() function after the critical section code, use this function to set the lock to "0".

__device__ int lock = 0;

__global__ void kernel() {
    
    
    while (atomicExch(&lock, 1) != 0);
    // 临界区代码
    atomicExch(&lock, 0);
}

2.2 Signal amount

A semaphore (Semaphore) is a common thread synchronization mechanism used to control concurrent access by multiple threads. CUDA provides a hardware-based implementation of semaphores called "CUDA Semaphore". The general steps for using CUDA semaphores are as follows:

1. Declare a variable of type int in the global memory as a semaphore.
2. Call the atomicAdd() function in the thread that needs to wait for the signal, and use this function to decrement the value of the semaphore by one.
3. Invoke the atomicAdd() function in the signaling thread to increment the semaphore value by one.
   For example, the following code shows how to create a binary semaphore with a counter of 1:

#include <semaphore.h>

sem_t mutex;
sem_init(&mutex, 0, 1);

In CUDA, you can use the sem_wait function to wait for a semaphore. If the value of the counter is 0, the thread will be blocked until other threads release the semaphore. For example, the following code shows how to implement a simple producer-consumer model using semaphores:

__device__ int buffer[10];
__device__ int count = 0;

__global__ void producer_consumer()
{
    
    
    while (true) {
    
    
        sem_wait(&mutex);
        if (count < 10) {
    
    
            buffer[count++] = threadIdx.x;
        }
        sem_post(&mutex);
    }
}

In this example, the producer_consumer function implements a simple producer-consumer model where multiple threads can write data to a shared buffer. The sem_wait function waits for the semaphore, if the buffer is not full, writes the data to the buffer, and increments the counter. The sem_post function releases the semaphore so that other threads can continue writing data.

2.3 Barriers

Barrier is a thread synchronization mechanism used to ensure that all threads are synchronized at a specified point. CUDA provides a hardware-based barrier implementation called "CUDA Barrier". When using a barrier, all threads are stopped until all threads have reached that point. This is useful for algorithms that require synchronization.
To use barriers, you can use the __syncthreads() function. The function waits for all threads to be at the same point, and only continues execution when all threads have reached that point. This is very useful for some algorithms that need to be synchronized.
Let's look at a simple example that demonstrates how to use barriers. In this example, each thread calculates the square of its own element in the array. To ensure that each thread has calculated its own value, we need to wait for all threads to complete, and at this point we insert a barrier. Here is a code example:

__global__ void square(float *d_out, float *d_in, int size) {
    
    
    int idx = threadIdx.x + blockDim.x * blockIdx.x;
    if (idx < size) {
    
    
        float val = d_in[idx];
        d_out[idx] = val * val;
    }
    __syncthreads();
}

In this example, we insert a barrier after each thread computes the square of its own element. This barrier ensures that all threads have finished their work, thus preventing any thread from accessing the result array until other threads have finished.

3. Summary

This article introduces concurrent programming and thread synchronization mechanisms in CUDA, including threads, locks, semaphores, and barriers. Understanding these mechanisms can help developers better utilize the parallel computing power of GPUs and avoid problems such as race conditions and deadlocks. In addition, CUDA also provides some advanced thread synchronization and communication mechanisms, such as mutexes, condition variables and events, which can be selected and used according to actual needs.

In the next article, we will take a deeper look at the memory model and memory management mechanisms in CUDA.