Hands-On GPU Programming with Python and CUDA

Summary

The main advantage of using a GPU over a CPU is its increased throughput, which means that we can execute more parallel code simultaneously on GPU than on a CPU; a GPU cannot make recursive algorithms or nonparallelizable algorithms somewhat faster. We see that some tasks, such as the example of building a house, are only partially parallelizable—in this example, we couldn't speed up the process of designing the house (which is intrinsically serial in this case), but we could speed up the process of the construction, by hiring more laborers (which is parallelizable in this case).

We used this analogy to derive Amdahl's Law, which is a formula that can give us a rough estimate of potential speedup for a program if we know the percentage ...