Explain the architectural differences between a CPU and a GPU and how these differences make GPUs suitable for high-performance computing.

CPUs and GPUs differ significantly in their architectural design, which directly impacts their suitability for different types of computational tasks. CPUs are designed for general-purpose computing, emphasizing low latency and high performance for single-threaded applications. They feature a relatively small number of cores, typically ranging from a few to dozens, each of which is complex and capable of executing a wide variety of instructions. These cores are optimized for sequential instruction processing, with features like branch prediction, out-of-order execution, and large caches to minimize latency and maximize single-thread performance. GPUs, on the other hand, are designed for parallel processing and are particularly well-suited for tasks that can be broken down into many independent operations. They have a massively parallel architecture, consisting of thousands of smaller, less complex cores. These cores are organized into Streaming Multiprocessors (SMs) in NVIDIA GPUs, and each SM can execute multiple threads concurrently. Unlike CPU cores, GPU cores are optimized for throughput, meaning they prioritize processing a large volume of data rather than minimizing the latency of individual operations. ....