1. 1. Python in Parallel
2. 2. Parallel
3. 1. 2.1. Types of Problems
   2. 2.2. Types of solutions
   3. 2.3. Time slicing
   4. 2.4. Concurrencies
   5. 2.5. How many parallels to use?
   6. 2.6. Dividing jobs
   7. 2.7. Performance Monitoring
4. 3. Threads
5. 1. 3.1. Python Threading docs
   2. 3.2. Threaded counters
   3. 3.3. Simple threaded counters
   4. 3.4. Simple threaded counters (parameterized)
   5. 3.5. Pass parameters to threads - Counter with attributes
   6. 3.6. Create a central counter
   7. 3.7. Lock - acquire - release
   8. 3.8. Counter - plain
   9. 3.9. GIL - Global Interpreter Lock
   10. 3.10. Thread load
   11. 3.11. Exercise: thread files
   12. 3.12. Exercise: thread URL requests.
   13. 3.13. Exercise: thread queue
   14. 3.14. Solution: thread queue
   15. 3.15. Solution: thread URL requests.
   16. 3.16. Concurrency
   17. 3.17. Create a counter queue
   18. 3.18. A Queue of tasks
6. 4. Forking
7. 1. 4.1. Fork
   2. 4.2. Forking
   3. 4.3. Fork skeleton
   4. 4.4. Fork with load
   5. 4.5. Fork load results
   6. 4.6. Marshalling / Serialization
   7. 4.7. Fork with random
   8. 4.8. Exercise: fork return data
   9. 4.9. Solution: fork return data
8. 5. Multiprocess
9. 1. 5.1. Multiprocess CPU count
   2. 5.2. Multiprocess N files: Pool
   3. 5.3. Multiprocess load
   4. 5.4. Multiprocess: Pool
   5. 5.5. Multiprocess load async
   6. 5.6. Multiprocess and logging
   7. 5.7. Exercise: Process N files in parallel
   8. 5.8. Exercise: Process N Excel files in parallel
   9. 5.9. Exercise: Fetch URLs in parallel
   10. 5.10. Exercise: Fetch URLs from one site.
   11. 5.11. Solution: Process N files in parallel
   12. 5.12. Solution: Fetch URLs in parallel
10. 6. Multitasking
11. 1. 6.1. What is Multitasking?
    2. 6.2. Multitasking example
    3. 6.3. Multitasking example with wait
    4. 6.4. Multitaksing - second loop waits for first one
    5. 6.5. Multitasking counter
    6. 6.6. Multitasking counter with thread locking