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@@ -61,7 +61,7 @@ A second is a duration, not an instant.
Two things can happen in the same second and not be simultaneous.
In computer science, this nuance matters.
With computers, this nuance matters.
* 9. Time
@@ -98,10 +98,12 @@ Source: [[https://www.prowesscorp.com/computer-latency-at-a-human-scale/]]
* 12. Cooking Recipes
- Step-by-step
- Concurrent
- Pre-heat oven
- Boil water
- Timers
- Concurrent:
- Handle other tasks while:
- Preheating oven
- Boiling water
- Waiting for timers
- Switching back to a task when it is ready for more work.
* 13. Multitasking
@@ -117,24 +119,28 @@ Computers can "simultaneously" work on:
Combination of:
- Operating System *Scheduler*
- Hardware (/interrupts/)
- Keyboard
- Filesystem
- Network Card
- Timer
* 17. Cooperative Scheduling
* 16. Cooperative Scheduling
[[file:images/cooperative-scheduling.jpg]]
* 18. Preemptive Scheduling
* 17. Preemptive Scheduling
[[file:images/preemptive-scheduling.jpg]]
* 19. Preemptive Scheduling
* 18. Preemptive Scheduling
[[file:images/preemptive-scheduling-tree.jpg]]
* 20. Multi Core
* 19. Multi Core
[[file:images/preemptive-scheduling-multi-core.jpg]]
* 21. Concurrency versus Parallelism
* 20. Concurrency versus Parallelism
- Parallelism :: performing computations at the same time.
- Multiple cores.
- Multiples computers.
- Multiple computers.
- Kind of like having multiple cooks in a kitchen.
- Can do different things at the same time.
- Can do one job faster (peeling a lot of potatoes).
@@ -142,13 +148,14 @@ Combination of:
- Concurrency :: performing computations over inter-leaving, non-sequential time periods.
- Single or multi-core.
* 22. Processes and Threads
* 21. Processes and Threads
A little more nuance.
- Different /recipes/
- Different /parts/ of a recipe
- Process :: a program in execution.
- Unique resources (memory, code, files, etc.).
- Start off with one thread.
- Can /fork/.
- Communicate via inter-process communication (IPC).
@@ -157,17 +164,17 @@ A little more nuance.
- Share resources ∴ less resource intensive.
- Can communicate via shared resources.
* 23. Processes and Threads
* 22. Processes and Threads
[[file:images/process-vs-thread.jpg]]
Source: https://stackoverflow.com/questions/16354460/forking-vs-threading/16354658
* 24. htop
* 23. htop
Press ~H~ to show threads.
* 25. Languages and Runtimes
* 24. Languages and Runtimes
- ~C~, ~C++~, ~Rust~ allow for fine-grained control of processes and threads.
#+begin_src C :output raw
@@ -180,7 +187,7 @@ int main() {
}
#+end_src
* 26. JavaScript
* 25. JavaScript
- Single-threaded (for application code).
- Runtimes implement concurrency via an event loop.
@@ -188,42 +195,7 @@ int main() {
Source: [[https://medium.com/preezma/node-js-event-loop-architecture-go-deeper-node-core-c96b4cec7aa4]]
* 27. Go
Runtime multi-threading, multiplexes goroutines onto threads.
[[file:images/go-scheduler.png]]
Source: https://freethreads.net/2019/01/23/go-runtime-scheduler-design-internals/
* 28. Quick Review
- Sequential versus Concurrent Steps
- Compare with recipes.
- Concurrency versus Parallelism
- Parallelism: multiple cooks, simultaneously /doing/.
- Concurrency: one or more cooks, simultaneously /dealing/ with many tasks.
- Operating System Scheduler
- Processes and Threads
- Languages and Runtimes
* 29. Part 2: Using Concurrency
Typical use cases:
- Network I/O
- Requests between clients and servers.
- Requests to a database.
- Requests between microservices.
- Requests to external APIs.
- User interfaces
- Capturing user input
- Rendering output
- Similar, but independent computations.
- Solving problems that are best expressed with concurrency.
* 30. JavaScript
* 26. Aside: Event Loop
What is the output of the following? Why?
#+begin_src javascript
@@ -238,14 +210,49 @@ setTimeout(() => console.log('2'), 0);
console.log('end');
#+end_src
* 31. Go Examples
* 27. Go
*Runtime scheduler* manages multiple threads.
[[file:images/go-scheduler.png]]
Source: https://freethreads.net/2019/01/23/go-runtime-scheduler-design-internals/
* 28. Quick Review
- Sequential versus Concurrent Steps
- Compare with recipes.
- Concurrency versus Parallelism
- Parallelism: multiple cooks, simultaneously /doing/.
- Concurrency: one or more cooks, /dealing/ with many tasks.
- Operating System Scheduler
- Processes and Threads
- Languages and Runtimes
* 29. Part 2: Using Concurrency
Typical use cases:
- Network I/O between:
- Clients and servers.
- Services and database(s).
- Microservices.
- Service and external APIs.
- User interfaces
- Capturing user input
- Rendering output
- Similar, but independent computations.
- Solving problems that are best expressed with concurrency.
* 30. Go Examples
1. Shared Memory with Mutex (Mutual Exclusion)
- Pitfall: synchronization, debugging.
2. Message Passing (Channels and Goroutines)
- Pitfall: resources usage, complexity.
* 32. Mutex Example
2. Message Passing (Channels and Goroutines)
- Pitfall: resource usage, complexity.
* 31. Mutex Example
Concurrent updates to a bank account balance.
Code:
@@ -253,31 +260,37 @@ Code:
Run: [[file:cmd/mutex/run.sh]]
- Without mutex.
- With mutex.
* 33. Data Races
* 32. Data Races
[[file:images/data-race.jpg]]
* 34. Mutex
* 33. Mutex
/Mutual Exclusion/
- Purpose: protect a critical section of code.
- Effect: serialization.
* 35. Race Detector
Run: [[file:cmd/mutex/run.sh]]
- With mutex.
* 34. Race Detector
How can we be sure?
Race detector can give us /some/ confidence (but no guarantees).
- Without mutex.
- With mutex.
* 36. Tradeoffs: Validating Correctness, Testing, Debugging
* 35. Tradeoffs: Validating Correctness, Testing, Debugging
- Non-deterministic behavior
- Works fine on one run, fatal bug in another.
- Up to how the scheduler(s) order things at execution time.
- Concealed bugs
* 37. Message Passing
* 36. Message Passing
Proverbs:
#+begin_quote
@@ -286,7 +299,7 @@ Don't communicate by sharing memory, share memory by communicating.
Channels orchestrate, mutexes serialize.
#+end_quote
* 38. Requirements
* 37. Program Requirements
- Read CSV of zip codes
- 50 in our example
@@ -298,27 +311,27 @@ Channels orchestrate, mutexes serialize.
- Display running list of top ten temperatures.
* 39. Concurrency Hallmarks
* 38. Concurrency Hallmarks
- Repeatedly processing similar data (lines of CSV)
- Network I/O
- UI I/O
* 40. Message Passing Example
* 39. Message Passing Example
Run: [[file:cmd/csp/run.sh]]
* 41. Code Walkthrough
* 40. Code Walkthrough
[[file:cmd/csp/main.go]]
* 42. Channels and Goroutines
* 41. Channels and Goroutines
[[file:images/message-passing-example-diagram.jpg]]
* 43. Channels and Goroutines
* 42. Channels and Goroutines
[[file:images/go-scheduler.jpg]]
* 44. Scaling
* 43. Scaling
What if:
- We wanted to process the entire list of 42,000 zip codes?
@@ -326,7 +339,7 @@ What if:
Could we just get rid of the limiter?
* 45. Resources
* 44. Resources
Probably not, because:
@@ -339,12 +352,12 @@ Probably not, because:
- Better data structures and algorithms could help here.
- Goroutines are cheap, but not free.
* 46. Possible Solutions
* 45. Possible Solutions
- Use a semaphore to limit concurrency.
- Buffers
* 47. Designing and Managing Concurrency
* 46. Designing and Managing Concurrency
- Size of input matters.
- Resources
@@ -353,7 +366,7 @@ Probably not, because:
- Backpressure
- Complexity
* 48. Quick Review
* 47. Quick Review
Techniques for implementing and managing concurrency:
- Event loop
@@ -367,7 +380,7 @@ Concurrency Pitfalls:
- Complexity
- Need to think differently about concurrent code.
* 49. Part 3: Infrastructure Concurrency
* 48. Part 3: Infrastructure Concurrency
Concurrent applications running concurrently on distributed computers.
@@ -376,20 +389,20 @@ Concurrent applications running concurrently on distributed computers.
- Databases
- Host and Network Infrastructure
* 50. Example
* 49. Example
[[file:images/infrastructure-data-race.jpg]]
* 51. Review
* 50. Review
- What concurrency is
- Sequential versus concurrent
- Comparison with recipes
- Comparison with recipes and cooks
- Compared to parallelism
- How it is implemented
- Operating System Scheduler and Interrupts
- Languages and Runtimes
* 52. Review
* 51. Review
- When, why, and how to use it
- I/O + UI
@@ -398,16 +411,16 @@ Concurrent applications running concurrently on distributed computers.
- Pitfalls
- Synchronization
- Resources Usage
- Resource Usage
- Debugging
- Complexity
* 53. Review
* 52. Review
- Infrastructure
- Data Race
* 54. Conclusion
* 53. Conclusion
- Goals:
- Give you a rough sense of what concurrency is.
@@ -421,7 +434,7 @@ Concurrent applications running concurrently on distributed computers.
- Tradeoffs and economics (resources, costs, developer time).
- Learning is continuous...
* 55. Further Reading
* 54. Further Reading
- [[https://slikts.github.io/concurrency-glossary/][Concurrency Glossary]]
- [[https://blog.golang.org/waza-talk][Concurrency is not parallelism]]