High Performance Python

Chapter 8. Concurrency

Questions You’ll Be Able to Answer After This Chapter

• What is concurrency and how is it helpful?
• What is the difference between concurrency and parallelism?
• Which tasks can be done concurrently and which can’t?
• What are the various paradigms for concurrency?
• When is the right time to take advantage of concurrency?
• How can concurrency speed up my programs?

I/O can be quite burdensome to the flow of a program. Every time your code reads from a file or writes to a network socket, it must pause to contact the kernel, request that the operation happen, and then wait for it to complete. This may not seem like the end of the world, especially after you realize that a similar operation happens every time memory is allocated; however, if we look back to Figure 1-3 we see that most of the I/O operations we perform are on devices that are orders of magnitude slower than the CPU.

For example, in the time it takes to write to a network socket, an operation that typically takes about 1 ms, we could have completed 2,400,000 instructions on a 2.4 GHz computer. Worst of all, our program is halted for much of this 1 ms time—our execution is paused and we are waiting for a signal that the write operation has completed. This time spent in a paused state is called “I/O wait.”

Concurrency helps us utilize this wasted time by allowing us to perform other operations while waiting for an I/O operation to complete. For example, in Figure 8-1 we see a depiction of a program that ...