Harnessing Parallelism: Exploring CUDA Threads and Grids#

Table of Contents#

1. Why Parallel Computing Matters
2. Overview of CUDA
3. CUDA Programming Model
4. Threads, Blocks, and Grids
5. Kernel Launch Configuration
6. Memory Hierarchy
7. Basic CUDA Example
8. Thread Indexing and Practical Usage
9. Synchronization and Barriers
10. Optimizing Thread and Block Configurations
11. Dynamic Parallelism
12. Best Practices and Common Pitfalls
13. Advanced Topics: Streams and Compute Capability
14. Real-World Examples and Professional-Level Expansions
15. Conclusion

Why Parallel Computing Matters#

Parallel computing is the art and science of breaking down a computational task into smaller pieces and performing these pieces simultaneously. Before the rise of parallel computing, CPUs followed Moore’s Law to achieve better performance—more transistors, higher clock speeds. But physical constraints, such as power consumption and heat dissipation, make constant clock-speed increases unsustainable. Instead, the shift toward multi-core and many-core architectures allows for greater total throughput even if frequency gains plateau.

GPUs are particularly well-suited for highly parallel tasks, as they feature:

• Hundreds to thousands of cores.
• A streamlined design that favors throughput.
• Specialized hardware for vectorized and graphics-oriented operations.

NVIDIA GPUs’ programmability via CUDA made them not just graphics accelerators but also general-purpose accelerators that handle workloads like matrix multiplications, deep learning, computational fluid dynamics, and more. Understanding how to leverage threads and grids in CUDA is the stepping stone to unlocking this computational power.

Overview of CUDA#

CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model devised by NVIDIA. It provides:

1. Extensions to the C/C++ (and by extension support in other languages like Fortran, Python, and more) that facilitate writing kernels (functions that run on the GPU).
2. Libraries and profiling tools that accelerate development.
3. A memory and execution model specifying how data moves between host (CPU) and device (GPU).

When writing CUDA code, you typically:

1. Write host code (CPU code), which sets up data, memory transfers, and launches the GPU kernels.
2. Write device code (GPU code), which executes kernels in parallel.
3. Configure grids and blocks to orchestrate how many threads execute the kernel in parallel.

This model grants developers intricate control over performance-critical aspects of their applications.

CUDA Programming Model#

Conceptually, the CUDA programming model can be viewed in two main layers:

1. Execution Model

   • The CPU (host) launches computational kernels to be executed on the GPU (device).
   • Each kernel launch spawns a grid of thread blocks, each containing multiple threads.
   • Threads within a block can cooperate via shared memory or synchronization primitives.

2. Memory Model

   • The GPU has multiple memory spaces: global, shared, local, constant, and texture memory.
   • Data typically starts on the CPU (host memory). It must be transferred to the GPU (device memory) before kernels operate on it.
   • After execution, results are transferred back to the host if needed.

The next sections delve deeper into these aspects, starting with the star of the show: the threads.

Threads, Blocks, and Grids#

CUDA organizes parallel work via a hierarchy:

1. Thread: The smallest unit of parallelism. Each thread executes the kernel code.
2. Block: A group of threads. Threads in the same block can synchronize with each other and share data via shared memory.
3. Grid: Formed by multiple blocks.

When you launch a kernel, you define the dimensions of the grid and the blocks:

• dim3 gridDim: The number of blocks in each dimension (1D, 2D, or 3D).
• dim3 blockDim: The number of threads in each block dimension (1D, 2D, or 3D).

The total number of threads launched is gridDim.x * gridDim.y * gridDim.z * blockDim.x * blockDim.y * blockDim.z.

Kernel Launch Configuration#

When invoking a kernel, you use a triple angle-bracket syntax. For example:

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// A simple kernel definition
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__global__ void myKernel(int *data) {
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    int idx = threadIdx.x + blockIdx.x * blockDim.x;
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    data[idx] = data[idx] * 2;
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}
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// Host code to launch the kernel
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int main() {
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    // Suppose N is the size of the array
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    const int N = 256;
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    // Pointers for host and device memory
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    int *h_data, *d_data;
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    // Allocate and initialize host data (omitted for brevity)
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    // Allocate device data
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    cudaMalloc(&d_data, N * sizeof(int));
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    // Copy data to device
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    cudaMemcpy(d_data, h_data, N * sizeof(int), cudaMemcpyHostToDevice);
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    // Configure the kernel launch
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    int blockSize = 128;
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    int gridSize = (N + blockSize - 1) / blockSize;
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    myKernel<<<gridSize, blockSize>>>(d_data);
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    // Copy results back
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    cudaMemcpy(h_data, d_data, N * sizeof(int), cudaMemcpyDeviceToHost);
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    cudaFree(d_data);
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    return 0;
30
}

In the above:

• gridSize is how many blocks in the grid.
• blockSize is number of threads in each block.
• threadIdx.x, blockDim.x, blockIdx.x are built-in variables in CUDA that allow each thread to figure out its global index.

The expression (N + blockSize - 1) / blockSize ensures that all elements are covered in case N is not perfectly divisible by blockSize.

Memory Hierarchy#

CUDA threads operate within a memory model with diverse latency and bandwidth characteristics. You must be aware of them to write efficient parallel programs:

1. Global Memory

   • Main memory on the device. Accessible by all threads in the grid.
   • Large, but with relatively high access latencies.

2. Shared Memory

   • Memory shared by threads within the same block.
   • Much faster than global memory (similar to an L1 cache), but limited in size.

3. Local Memory

   • Private to each thread. Used for register spill-over or local arrays.
   • Has similar latencies to global memory.

4. Constant and Texture Memory

   • Read-only caches that may provide benefits under specific access patterns.

5. Registers

   • Each Thread’s fastest memory, but also a limited resource.
   • Overuse can cause register spills into local memory, slowing performance.

Efficient kernel design often involves:

• Loading data from global memory and reusing it in shared memory.
• Minimizing uncoalesced accesses to global memory.
• Ensuring enough threads to hide memory latency.

Basic CUDA Example#

Let’s start with a straightforward “hello world” type of program in CUDA—vector addition:

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#include <iostream>
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#include <cuda.h>
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__global__ void vectorAdd(const float* A, const float* B, float* C, int N) {
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    int i = blockIdx.x * blockDim.x + threadIdx.x;
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    if (i < N) {
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        C[i] = A[i] + B[i];
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    }
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}
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int main() {
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    const int N = 1024;
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    const int size = N * sizeof(float);
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    // Host memory allocation
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    float *h_A, *h_B, *h_C;
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    h_A = (float *)malloc(size);
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    h_B = (float *)malloc(size);
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    h_C = (float *)malloc(size);
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    // Initialize host arrays
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    for(int i = 0; i < N; i++){
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        h_A[i] = i * 1.0f;
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        h_B[i] = i * 2.0f;
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    }
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    // Device memory allocation
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    float *d_A, *d_B, *d_C;
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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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    // Transfer data from host to device
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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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    // Kernel launch configuration
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    int blockSize = 256;
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    int gridSize  = (N + blockSize - 1) / blockSize;
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    vectorAdd<<<gridSize, blockSize>>>(d_A, d_B, d_C, N);
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    // Transfer results back
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    cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);
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    // Clean up
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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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    // Check results
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    for(int i = 0; i < 5; i++){
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        std::cout << "C[" << i << "] = " << h_C[i] << std::endl;
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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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}

Key points:

• We define the kernel vectorAdd using __global__.
• Each thread computes one element of vector C, based on its global index.
• We verify the results on the host.

Thread Indexing and Practical Usage#

Thread indexing in CUDA allows each thread to independently handle different elements of our data set. Typically, you compute a global index:

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int i = threadIdx.x + blockIdx.x * blockDim.x;

In 2D or 3D cases, you might have:

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int col = threadIdx.x + blockIdx.x * blockDim.x;
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int row = threadIdx.y + blockIdx.y * blockDim.y;

This approach generalizes to higher dimensions. Here is an illustrative table:

By using these indices, you ensure each thread computes a distinct portion of the workload. This is essential for data parallel tasks.

Synchronization and Barriers#

Within a block, threads can interact in ways that require synchronization:

• __syncthreads(): A barrier function that pauses execution of all threads in a block until every thread has reached that point.
• Memory fence functions (__threadfence_block(), __threadfence(), etc.) can be used for finer-grained control over memory operations.

Because blocks execute independently, CUDA provides limited means for synchronizing across different blocks within a single kernel. Usually, to achieve global synchronization (across all threads in all blocks), you end your kernel and then launch another kernel—ensuring all blocks from the previous kernel have finished.

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__global__ void reduceSum(float *input, float *output, int N) {
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    extern __shared__ float s_data[];
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    int tid = threadIdx.x;
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    int idx = blockIdx.x * blockDim.x + tid;
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    // Load input into shared memory
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    s_data[tid] = (idx < N) ? input[idx] : 0.0f;
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    __syncthreads();
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    // Perform reduction within the block
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    for(int stride = blockDim.x / 2; stride > 0; stride >>= 1) {
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        if(tid < stride) {
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            s_data[tid] += s_data[tid + stride];
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        }
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        __syncthreads();
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    }
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    // Write result for this block to global memory
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    if (tid == 0) {
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        output[blockIdx.x] = s_data[0];
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    }
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}

Optimizing Thread and Block Configurations#

Choosing optimal thread and block dimensions is crucial for performance. Some factors to consider:

1. Occupancy

   • Occupancy is the ratio of active warps to the maximum possible warps on an SM (Streaming Multiprocessor).
   • High occupancy often hides memory latency but does not always guarantee maximum performance.

2. Memory Coalescing

   • For maximum global memory bandwidth, threads in a warp should access consecutive addresses.

3. Shared Memory Usage

   • Ensure you have enough shared memory per block if you rely on it heavily.

4. Register Usage

   • Each thread uses a certain amount of registers. Exceeding the hardware limits forces register spills.

5. Warp Synchronous Execution

   • Threads in a warp (32 threads on most GPUs) execute instructions in lockstep.

As an example, if you are performing a simple operation, you might use a large number of blocks each with 256 or 512 threads to ensure many warps are active, thus hiding memory latencies.

Dynamic Parallelism#

Dynamic Parallelism is an advanced feature that allows one kernel to launch another kernel directly on the GPU, without going back to the CPU for new kernel launches. This can simplify algorithms that dynamically generate more work as they progress.

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__global__ void childKernel(...) {
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    // ...
3
}
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__global__ void parentKernel(...) {
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    // ...
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    // Launch child kernel from GPU
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    childKernel<<<gridDim, blockDim>>>(...);
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    // ...
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}

This approach can be advantageous in irregular applications, such as graph traversals where new tasks are discovered as you proceed. However, launching kernels from the device can incur overhead and under some circumstances might not show performance benefits compared to a well-structured series of host-launched kernels.

Best Practices and Common Pitfalls#

1. Allocate/Free Device Memory Wisely

   • Repeatedly allocating and freeing memory in the middle of time-sensitive code can be costly.

2. Overuse of Shared Memory

   • Shared memory is limited per block. Over-allocation can reduce occupancy.

3. Check for CUDA Errors

   • Use cudaGetLastError() and cudaDeviceSynchronize() to catch runtime errors.

4. Host-Device Transfer Bottlenecks

   • Minimize data transfers between CPU and GPU. Overheads can degrade performance if you frequently copy data.

5. Thread Divergence

   • If threads within a warp follow different branches of execution, you get divergence. This can reduce performance.

6. Uncoalesced Global Memory Access

   • Non-sequential memory accesses by a warp hamper memory throughput. Strive to align and coalesce accesses.

7. Grid Synchronization

   • Remember that only block-level synchronization is provided within a single kernel. Use subsequent kernel launches as global synchronization points if needed.

Advanced Topics: Streams and Compute Capability#

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cudaStream_t stream0, stream1;
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cudaStreamCreate(&stream0);
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cudaStreamCreate(&stream1);
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// Launch kernel in stream0
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myKernel<<<gridSize, blockSize, 0, stream0>>>(...);
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// Launch another kernel or memory copies in stream1
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cudaMemcpyAsync(..., cudaMemcpyHostToDevice, stream1);
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anotherKernel<<<gridSize, blockSize, 0, stream1>>>(...);
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// Wait for streams to complete before final operations
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cudaStreamSynchronize(stream0);
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cudaStreamSynchronize(stream1);
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cudaStreamDestroy(stream0);
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cudaStreamDestroy(stream1);

Real-World Examples and Professional-Level Expansions#

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__global__ void convolution2D(const float* input, float* output,
2
                              const float* filter, int width, int height, int filterSize) {
3
    int col = blockIdx.x * blockDim.x + threadIdx.x;
4
    int row = blockIdx.y * blockDim.y + threadIdx.y;
5

6
    if(row < height && col < width) {
7
        float result = 0;
8
        int kRadius = filterSize / 2;
9
        for(int fRow = -kRadius; fRow <= kRadius; fRow++) {
10
            for(int fCol = -kRadius; fCol <= kRadius; fCol++) {
11
                int imgRow = row + fRow;
12
                int imgCol = col + fCol;
13
                if(imgRow >= 0 && imgRow < height && imgCol >= 0 && imgCol < width) {
14
                    float pixel = input[imgRow * width + imgCol];
15
                    float coeff = filter[(fRow + kRadius)*filterSize + (fCol + kRadius)];
16
                    result += pixel * coeff;
17
                }
18
            }
19
        }
20
        output[row * width + col] = result;
21
    }
22
}

Conclusion#

In this post, we have explored the fundamentals of GPU programming with CUDA, focusing on:

• How threads, blocks, and grids form the bedrock of parallelism.
• Memory hierarchies and their effects on performance.
• Basic and advanced kernel launch configurations.
• Synchronization details, best practices, and optimization strategies.
• Professional-level expansions, such as dynamic parallelism, Tensor Cores, and streams.

Harnessing the full power of CUDA requires an ongoing process of experimentation, profiling, and optimization. By mastering the concepts of threads and grids, you have taken a significant step toward building high-performance GPU applications. Keep exploring advanced features, test on real-world workloads, and refine your approach to fully unlock the computational capabilities offered by modern GPUs.