Overview
When you start learning CUDA, one of the first things you notice is that your program no longer runs only on the CPU. Instead, the CPU and GPU work together, each handling a different part of the task. This way of working is what allows CUDA applications to process large amounts of data efficiently.
In this article, we’ll explore the CUDA programming model and how it defines the relationship between the CPU and GPU. We’ll then look at how a CUDA program runs in practice and how memory is managed during execution. Understanding these ideas early will help you write correct, efficient, and well-structured CUDA programs.
CUDA Programming Model
The CUDA programming model is designed for parallel computing and is based on a clear separation between two components: the host and the device. The host is usually the CPU and is responsible for controlling the program, while the device is typically the GPU, which performs computations in parallel.
A key idea in this model is that the host and device have separate memory spaces. Data stored in CPU memory is not directly accessible by the GPU, and device memory cannot be accessed directly by the CPU. Because of this, the CUDA programming model requires developers to explicitly manage memory and data movement between the host and the device.
This host–device separation might feel unfamiliar at first, but it gives developers precise control over performance. Once this structure is clear, the next step is understanding how both sides interact when a CUDA program is executed.
How the CUDA Programming Model Executes Programs
Knowing the roles of the host and device explains where work happens, but it does not yet explain how the program runs. In practice, applications built using the CUDA programming model follow a well-defined execution flow that coordinates work between the CPU and GPU.
This execution flow, often called the CUDA execution model, describes how data is prepared, processed on the GPU, and returned to the CPU. Let’s break it down into simple steps.
CUDA Execution Sequence
The execution sequence shows how a typical CUDA program moves from start to finish.
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Data Transfer: Execution begins on the CPU. Any data that the GPU needs is copied from host memory to device memory. Since the GPU operates only on its own memory, this step prepares the data for parallel processing.
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Kernel Invocation: Once the data is available on the GPU, the CPU launches a kernel. A kernel is a function that runs on the GPU and is executed by many threads at the same time. Each thread works on a small part of the data, which is the foundation of parallel programming with CUDA.
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Data Retrieval: After the kernel completes execution, the results are copied back from device memory to host memory. The CPU can then continue with the rest of the application. This pattern—copying data to the GPU, executing kernels, and copying results back—is central to the CUDA programming model. Because memory movement plays such an important role in this process, it naturally leads us to the topic of memory management.
Memory Management in the CUDA Programming Model
Memory management is a core part of the CUDA programming model. Since the CPU and GPU use separate memory spaces, developers must explicitly allocate, initialize, and free memory on the device.
CUDA provides a small set of APIs that make this process manageable. The most commonly used functions are cudaMalloc, cudaMemcpy, cudaMemset, and cudaFree. Together, these functions form the basis of the CUDA memory model.
- cudaMalloc: Before the GPU can process any data, memory must be allocated on the device. This is done using cudaMalloc, which works in a similar way to malloc on the CPU
int* d_data; // Pointer for device memory
int size = 100 * sizeof(int); // Allocate space for 100 integers
cudaMalloc((void**)&d_data, size); // Allocate memory on the GPU- cudaMemcpy: Data movement between the host and device is handled using cudaMemcpy. The direction of the copy must be specified explicitly, which reflects the host–device separation defined by the CUDA programming model.
int h_data[] = {1, 2, 3, 4, 5};
int* d_data;
int dataSize = sizeof(h_data);
// Allocate memory on the GPU
cudaMalloc((void**)&d_data, dataSize);
// Copy data from host to device
cudaMemcpy(d_data, h_data, dataSize, cudaMemcpyHostToDevice);
// Perform GPU operations with d_data
// Copy results back from device to host
cudaMemcpy(h_data, d_data, dataSize, cudaMemcpyDeviceToHost);- cudaMemset: In many cases, device memory needs to be initialized before kernel execution. cudaMemset is used to set a block of device memory to a specific value, commonly zero.
int size = 5 * sizeof(int);
int* d_data;
// Utilize cudaMemset to set all elements in the deviceArray to 0
cudaMemset(d_data, 0, size);- cudaFree: After GPU computation is complete, device memory should be released using cudaFree. Proper memory cleanup is important to avoid memory leaks and to keep GPU resources available.
// Deallocate the assigned memory using cudaFree
cudaFree(d_data);Summary
In this article, we explored the CUDA programming model and how it divides work between the CPU and GPU. We looked at how a CUDA program executes step by step and why explicit memory management is an important part of GPU programming. With these fundamentals in place, you’re now better prepared to understand how CUDA uses threads and kernels to achieve parallel performance.
What’s Next
At this point, you should have a solid understanding of the CUDA programming model, how a CUDA application moves through its execution steps, and how data is managed between the CPU and GPU. These ideas are tightly connected and together form the backbone of every CUDA program.
In the next part of this series, we’ll take the next logical step and look at how computation actually scales on the GPU. We’ll explore how work is divided among thousands of GPU threads, how thread hierarchy and indexing help each thread know what it should process, and how kernels bring everything together for parallel execution.
