Hands-On GPU Programming with Python and CUDA

Chapter 5, Streams, Events, Contexts, and Concurrency

1. The performance improves for both; as we increase the number of threads, the GPU reaches peak utilization in both cases, reducing the gains made through using streams.
2. Yes, you can launch an arbitrary number of kernels asynchronously and synchronize them to with cudaDeviceSynchronize.
3. Open up your text editor and try it!
4. High standard deviation would mean that the GPU is being used unevenly, overwhelming the GPU at some points and under-utilizing it at others. A low standard deviation would mean that all launched operations are running generally smoothly.
5. i. The host can generally handle far fewer concurrent threads than a GPU. ii. Each thread requires its own CUDA context. The GPU can ...