CUDA and parallel computing: threads, blocks and kernels on a GPU
CUDA is NVIDIA's platform for accelerated computing. It allows developers to write kernels that run on many GPU threads, organized into blocks, to speed up compute-intensive tasks. This article explains the core concepts and how to get started.
What CUDA is
CUDA is NVIDIA's platform for accelerated computing and provides the software layer that lets applications use GPUs for parallel tasks. NVIDIA's CUDA platform page describes it as the foundation for GPU computing.
The CUDA Toolkit includes a compiler, runtime library, debugging tools, and GPU-accelerated libraries. Developers can also use CUDA Python for AI and high-performance computing applications.
CUDA is widely used in deep learning, scientific computing, and high-performance computing, enabling applications to run much faster than with CPU-only processing.
The CUDA execution model
CUDA's programming model is based on kernels, functions that run on the GPU. A kernel is launched with a special syntax that specifies how threads are arranged. Threads are grouped into blocks, and blocks form a grid, allowing the kernel to execute over many parallel threads.
The CUDA Programming Guide is the official resource for understanding the CUDA programming model. It states that CUDA is a parallel computing platform that enables dramatic performance increases by using the GPU.
In practice, a developer writes a kernel with the __global__ qualifier and sets the launch configuration with triple angle brackets (<<< >>>), which determines the number of blocks and threads per block.
Writing and launching a CUDA kernel
A typical CUDA program follows a few steps. First, write a kernel function for the computation that runs on the GPU. Then allocate memory that both the CPU and GPU can access. Launch the kernel with the desired thread configuration, synchronize the CPU with the GPU, and read the results.
NVIDIA's introductory example demonstrates this. A simple array addition function becomes a kernel with __global__. Unified memory is allocated with cudaMallocManaged, giving a pointer usable from both host and device code. The kernel is launched as add<<<1, 1>>>(N, x, y), launching one thread. The host then calls cudaDeviceSynchronize to wait for completion, as explained in the blog post.
This simple example can be scaled to launch thousands of threads by increasing the block and grid dimensions, making it easy to port existing C++ code to the GPU.
Optimizing CUDA applications
The CUDA C++ Best Practices Guide introduces a design cycle called APOD: Assess, Parallelize, Optimize, Deploy. This cycle helps developers identify the parts of an application that benefit most from GPU acceleration and improve performance iteratively.
The assessment step locates code that consumes the most execution time. Parallelization can involve calling an existing GPU-optimized library such as cuBLAS or cuFFT, or refactoring code to expose parallelism. Optimization covers techniques like overlapping data transfers with computation and fine-tuning floating-point operations, guided by profiling tools.
By following APOD, developers can achieve incremental speedups and deploy faster versions of their applications in production.
Parallel computing context and alternatives
Parallel computing means splitting a large problem into smaller parts that multiple processors handle at the same time, according to IBM's explanation of parallel computing. That method is much faster than serial computing, where one processor does steps one after another.
GPUs are often deployed alongside CPUs to expand data throughput and run more calculations at once. CUDA is one platform for GPU programming, while AMD offers its own open software stack called ROCm, which provides runtimes, compilers, and libraries for AMD GPUs, as described in the AMD ROCm documentation.
Both CUDA and ROCm enable developers to write high-performance applications that use GPU parallelism, though they target different hardware.
| Component | Purpose |
|---|---|
| CUDA Toolkit | Provides compiler, runtime library, and debugging tools. |
| CUDA-X libraries | Prebuilt libraries for AI, HPC, and other domains. |
| Nsight tools | Help build, debug, and profile GPU applications. |
| CUDA Python | Allows building GPU applications directly in Python. |
Frequently asked questions
What is a CUDA kernel?
A CUDA kernel is a function that runs on the GPU and is called from CPU code. It is defined with the __global__ qualifier.
How are CUDA threads organized?
Threads are grouped into blocks, and blocks form a grid. The launch configuration specifies the number of blocks and threads per block.
What is unified memory in CUDA?
Unified memory provides a single memory space accessible by both the CPU and GPU, simplifying memory management in CUDA programs.
Why is parallel computing faster than serial computing?
Parallel computing breaks a problem into smaller parts that can be solved simultaneously by multiple processors, reducing overall execution time compared to solving sequentially with one processor.