As AI enthusiasts, we know that choosing the right version of CUDA can make a significant difference in our performance and efficiency when working with deep learning frameworks like TensorFlow or PyTorch.

First, let’s take a look at what’s new in each release:

– CUDA 11.4 (released on September 20th, 2021) introduced support for NVIDIA Ampere architecture GPUs and improved performance for various deep learning operations such as convolution and matrix multiplication. It also added support for the new Tensor Core VT (Tensor Core Vectorized Tile) instruction set, which can improve throughput by up to 50% compared to previous versions of CUDA.

– CUDA 11.5 (released on March 23rd, 2022) added support for NVIDIA’s new H100 GPU and introduced a number of performance improvements across various deep learning frameworks such as TensorFlow, PyTorch, and MXNet. It also improved the accuracy of floating-point arithmetic by introducing a new “mixed precision” mode that can reduce training time by up to 5x compared to single-precision training.

– CUDA 11.6 (released on November 2nd, 2022) added support for NVIDIA’s new A40 GPU and introduced a number of performance improvements across various deep learning frameworks such as TensorFlow, PyTorch, and MXNet. It also improved the accuracy of floating-point arithmetic by introducing a new “mixed precision” mode that can reduce training time by up to 5x compared to single-precision training.

Now let’s take a closer look at API compatibility between these versions:

– CUDA 11.4 and 11.5 are fully compatible with each other, meaning that code written for one version can be easily ported to the other without any changes required. This is because both versions share the same major release number (CUDA 11) and minor release numbers (version 4 or 5).

– CUDA 11.6 is also fully compatible with CUDA 11.4 and 11.5, but it introduces some new features that may require changes to existing code. For example, the new “mixed precision” mode introduced in CUDA 11.5 requires a specific API call (cudaMallocManaged) to be used when allocating memory for tensors. This is because managed memory can automatically convert between single-precision and half-precision floating-point data types, which can significantly reduce training time and improve accuracy.

In terms of performance, CUDA 11.6 generally outperforms both CUDA 11.4 and 11.5 due to the introduction of new features such as Tensor Core VT and support for NVIDIA’s A40 GPU. However, this may not always be the case depending on the specific workload being used.

Regardless of which version you choose, it’s always important to test and benchmark your code to ensure that performance is optimized for your specific use case. And as always, make sure to consult the official CUDA documentation for more information on API compatibility between different versions.