Which tools or frameworks should be integrated into a system using multiple NVIDIA GPUs in a distributed environment to optimize workload distribution?

The integration of NVIDIA CUDA into a system utilizing multiple NVIDIA GPUs in a distributed environment is crucial for optimizing workload distribution. CUDA is a parallel computing platform and application programming interface (API) model that allows developers to harness the power of NVIDIA GPUs for general purpose processing. By providing low-level access to the GPU resources, CUDA enables more efficient computation by allowing developers to design algorithms that can effectively distribute and parallelize workloads across multiple GPUs.

In a distributed environment, specifically when dealing with multiple GPUs, CUDA's ability to manage memory and operational tasks across the hardware becomes invaluable. It allows for optimized data transfer and synchronization between GPUs, which is essential for performance, especially in applications such as deep learning and scientific simulations where calculations are intensive and can be parallelized.

While Keras and TensorFlow Serving are useful for building and deploying machine learning models, they do not directly address the optimization of workload distribution across multiple GPUs. These tools sit at a higher level of abstraction and primarily handle model training and serving but do not engage in the low-level processing and optimizations that CUDA provides. In contrast, NVIDIA NCCL is indeed another important library for multi-GPU communication, particularly for collective operations among GPUs in distributed training. However, as a foundational layer, CUDA is the key