1. The Brave New World of Software Adam Kemp Staff Software Engineer National Instruments

2. Back in my day… Programmers had it tough… – No fancy high-level languages (except C…) – What’s an “IDE”? – Debuggers? Just use printf – No Internet (i.e., no Google) Good for you, old man…

3. Back to reality Programming is still just as hard Problems keep getting harder as tools get better The tools are never good enough Also applies to hardware

4. Hardware comparison RAM – Then: 640 kB should be enough for anyone – Now: 2 GB minimum Clock speed: – Then: 8-10 MHz (“turbo mode”) – Now: 2-3 GHz

5. Moore’s Law The number of transistors on a chip will double about once every 2 years

6. Clock speed reality check 3 GHz in 2002 – => 48 GHz next year?

7. Clock speed history

8. Speed limits Heat Power

9. How is that a software problem? “I hate it when the computer gets in the way of my computer science.” – Some LabVIEW developer

10. “Intel pledges 80 cores in five years” - Headline following Intel Developer Forum September 2006 The solution: Multi-core Dual-core Quad-core N-core

11. How can software keep up? Optimize – Only goes so far Do more in parallel

12. Programming the old way: void doSomeStuff() { doTask1(); doTask2(); }

13. Programming the new way: void doSomeStuff() { startTask1(); startTask2(); waitForTasks(); } startTask1() startTask2() startTask1() startTask2() doTask2() doTask1() waitForTasks()

14. Threads Threads are tasks that can be run at the same time as other tasks startTask1() startTask2() startTask1() startTask2() doTask2() doTask1() waitForTasks() Thread 1 Thread 2 Thread 3

15. Programming Paradigms Procedural/Imperative – Sequence of steps executed in order – Difficult to visualize multiple threads – Difficult to write multiple threads – Even harder to write safe code for multiple threads “Probably the greatest cost of concurrency is that concurrency really is hard...” – Herb Sutter

16. Programming Paradigms Dataflow – Emphasis on data – Data “flows” through the program

17. Dataflow Example Control/indicator terminals Wires Primitives (built-in functions)

18. Dataflow Data flows through “wires” – Produced by output terminals – Consumed by input terminals Execution order defined by flow – A node runs when it has all of its inputs – A node’s outputs are available only after it runs

19. Dataflow Example

20. Thread 1 Thread 2 Thread 3

21. Conclusion Programming is just as hard today Hardware is not getting faster Parallel programming is the future Dataflow may be a better way to do parallel programming

22. Advice Learn on your own – Keep up with tech trends – Work on your own pet projects – Work with other people Get an internship Try LabVIEW – http://www.ni.com/labview/ http://www.ni.com/labview/