What does CUDA Graph with Fusion optimize in GPU tasks?

The correct answer focuses on the optimization of kernel launches and memory operations in GPU tasks through CUDA Graph with Fusion. This approach is particularly significant because it allows developers to define a series of operations as a single task graph. By doing this, multiple operations can be executed without the overhead typically associated with launching kernels and managing memory transfers.

CUDA Graphs can minimize the number of interactions with the GPU by allowing the execution of multiple operations in one go, thereby reducing launch overhead and optimizing memory transfers. The fusion aspect refers to merging several operations into a single kernel execution, which further enhances performance by leveraging the GPU's resources more effectively.

This optimization is crucial for improving the performance of GPU-accelerated applications, as it directly affects the efficiency with which data is processed and the overall speed of computations. Other options like data storage management, error detection and correction, and performance monitoring, while important in the context of GPU tasks, do not specifically align with the primary focus of CUDA Graph and Fusion, which is centered on optimizing kernel and memory operation efficiencies.