CCCL Runtime: A Modern C++ Runtime for CUDA

The NVIDIA CUDA Core Compute Libraries (CCCL) introduce a modernized runtime for CUDA developers, combining the power of C++ with the flexibility of Python. This new framework enhances the developer experience by providing efficient abstractions that simplify CUDA programming.

What is CCCL Runtime?

CCCL Runtime offers a set of idiomatic C++ APIs designed to encapsulate core CUDA functionalities, including stream management, memory allocation, and kernel launches. Unlike the conventional NVIDIA CUDA runtime, which was built as a convenience layer over the CUDA driver API, CCCL aims to provide a more contemporary design that aligns with modern C++ standards.

Key Features of CCCL

The CCCL Runtime includes a collection of headers such as <cuda/stream>, <cuda/buffer>, and <cuda/launch>. These headers leverage advanced C++ features to create robust and convenient abstractions, moving beyond the limitations of the traditional C source compatibility of the original CUDA runtime.

The design of CCCL incorporates valuable insights gained from over two decades of CUDA's evolution, ensuring that the API is both modern and efficient.

Compatibility and Incremental Adoption

One of the standout features of the CCCL Runtime is its compatibility helpers. These tools allow developers to adopt the new runtime incrementally, enabling them to incorporate it without the need for extensive rewrites of existing code that utilizes the traditional CUDA runtime API.

Addressing Complexity in CUDA Programs

As CUDA applications become increasingly complex—with multiple libraries sharing devices, streams, and memory—the demand for clear and composable APIs grows. CCCL Runtime is specifically designed to address this need, allowing developers to manage dependencies more transparently.

Example: Vector Addition with CCCL

To illustrate the practical use of CCCL Runtime, let's look at a classic vector addition example. This example showcases the overall structure and highlights the differences introduced by the new APIs.

#include <cuda/buffer>
#include <cuda/devices>
#include <cuda/launch>
#include <cuda/memory_pool>
#include <cuda/std/span>
#include <cuda/stream>

struct kernel {
    template <typename Config>
    __device__ void operator()(Config config, cuda::std::span<const int> A, cuda::std::span<const int> B, cuda::std::span<int> C) {
        auto tid = cuda::gpu_thread.rank(cuda::grid, config);
        if (tid < A.size()) C[tid] = A[tid] + B[tid];
    }
};

int main() {
    // 1. Devices and streams
    cuda::device_ref device = cuda::devices[0];
    cuda::stream stream{device};

    // 2. Memory allocation
    auto pool = cuda::device_default_memory_pool(device);
    int num_elements = 1000;
    auto A = cuda::make_buffer<int>(stream, pool, num_elements, 1);
    auto B = cuda::make_buffer<int>(stream, pool, num_elements, 2);
    auto C = cuda::make_buffer<int>(stream, pool, num_elements, cuda::no_init);

    // 3. Kernel launch
    constexpr int threads_per_block = 256;
    auto config = cuda::distribute<threads_per_block>(num_elements);
    cuda::launch(stream, config, kernel{}, A, B, C);

    // Make the CPU thread wait for the GPU work to finish.
    sync();
    return 0;
}

Breaking Down the Example

This simple vector addition example can be divided into three main sections: initializing devices and streams, allocating memory, and launching the kernel. Each step highlights how CCCL enhances the CUDA programming model.

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For product and engineering leaders, the practical question is how this could reshape roadmaps, vendor choices, and security reviews over the next few quarters.

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