In this chapter, we looked at how to initialize a stream of random numbers in cuRAND by choosing the appropriate seed. Since computers are deterministic devices, they can only generate lists of pseudo-random numbers, so our seed should be something truly random; generally, adding a thread ID to the clock time in milliseconds will work well enough for most purposes.

We then looked at how we can use the uniform distribution from cuRAND to do a basic estimate of Pi. Then we took on a more ambitious project of creating a Python class that can compute definite integrals of arbitrary functions; we used some ideas from metaprogramming coupled with the CUDA Math API to define these arbitrary functions. Finally, we had a brief overview of the CUDA Thrust library, which is generally used for writing pure CUDA C programs outside of Python. Thrust most notably provides a device_vector container that is similar to the standard C++ vector. This reduces some of the cognitive overhead from using pointers in CUDA C.

Finally, we looked at a brief example of how to use Thrust with an appropriate functor to do simple point-wise and reduce operations, in the form of the implementation of a simple dot product function.