Lecture 35: Real-World USE OF Graphs

1. CSC 213 – Large Scale Programming Lecture 35:Real-World USE OF Graphs

2. Today’s Goals • Consider what new does & how Java works • What are traditional means of managing memory? • Why did they change how this was done for Java? • What are the benefits & costs of these changes? • Examine real-world use of graphs& its benefits • How do all of those graph algorithms get used? • Can we take advantage of this knowledge somehow? • What occurs in real-world we have not covered? • And why is beer ALWAYS answer to life’s problems

3. Explicit Memory Management • Traditional form of memory management • Used a lot, but fallen out of favor • malloc / new • Commands used to allocate space for an object • free / delete • Return memory to system using these command • Simple to use

4. Explicit Memory Management • Traditional form of memory management • Used a lot, but fallen out of favor • malloc / new • Commands used to allocate space for an object • free / delete • Return memory to system using these command • Simple to use, but tricky to get right • Forget to freememory leak • free too soon dangling pointer

5. Dangling Pointers Node x = new Node(“happy”);

6. Dangling Pointers Node x = new Node(“happy”); Node ptr = x;

7. Dangling Pointers Node x = new Node(“happy”); Node ptr = x; delete x;// But I’m not dead yet!

8. Dangling Pointers Node x = new Node(“happy”); Node ptr = x; delete x;// But I’m not dead yet! Node y = new Node(“sad”);

9. Dangling Pointers Node x = new Node(“happy”); Node ptr = x; delete x;// But I’m not dead yet! Node y = new Node(“sad”); cout << ptr.data << endl;// sad 

10. Dangling Pointers Node x = new Node(“happy”); Node ptr = x; delete x;// But I’m not dead yet! Node y = new Node(“sad”); cout << ptr.data << endl;// sad  • Creates insidious, hard-to-find bugs

11. Solution: Garbage Collection • Allocate objects into program’s heap • No relation to heap implementing a priority queue • This heap is simply a “pile of memory” • Garbage collector scans objects on heap • Starts at references in program stack & static fields • Finds objects reachable from those program roots • We consider the unreachable objects “garbage” • Cannot be used again, so safe to remove from heap • Need to include free command is eliminated

12. No More Dangling Pointers Node x = new Node(“happy”);

13. No More Dangling Pointers Node x = new Node(“happy”); Node ptr = x;

14. No More Dangling Pointers Node x = new Node(“happy”); Node ptr = x; //xreachable throughptrso cannot reclaim!

15. No More Dangling Pointers Node x = new Node(“happy”); Node ptr = x; //xreachable throughptrso cannot reclaim! Node y = new Node(“sad”);

16. No More Dangling Pointers Node x = new Node(“happy”); Node ptr = x; //xreachable throughptrso cannot reclaim! Node y = new Node(“sad”); cout << ptr.data << endl;// happy! 

17. No More Dangling Pointers Node x = new Node(“happy”); Node ptr = x; //xreachable throughptrso cannot reclaim! Node y = new Node(“sad”); cout << ptr.data << endl;// happy!  • Eliminates one mistake programmers make! • But how do we perform garbage collection?

18. Garbage Collection HEAP • Static & locals are called root references • Must compute objects in their transitive closure

19. Garbage Collection HEAP • Static & locals are called root references • Must compute objects in their transitive closure

20. Garbage Collection HEAP • Static & locals are called root references • Must compute objects in their transitive closure

21. Garbage Collection HEAP • Static & locals are called root references • Must compute objects in their transitive closure

22. Garbage Collection HEAP • Static & locals are called root references • Must compute objects in their transitive closure

23. Garbage Collection HEAP • Static & locals are called root references • Must compute objects in their transitive closure

24. Garbage Collection HEAP • Static & locals are called root references • Must compute objects in their transitive closure

25. Garbage Collection HEAP • Static & locals are called root references • Must compute objects in their transitive closure

26. Garbage Collection HEAP • Static & locals are called root references • Must compute objects in their transitive closure

27. Garbage Collection HEAP • Static & locals are called root references • Must compute objects in their transitive closure

28. Garbage Collection HEAP • Static & locals are called root references • Must compute objects in their transitive closure

29. Garbage Collection HEAP • Remove unmarked objects from the heap

30. Garbage Collection HEAP • Remove unmarked objects from the heap

31. Garbage Collection HEAP • Remove unmarked objects from the heap • New objects allocated into empty spaces

32. Why Not Always Use GC? • Garbage collection has obvious benefits • Eliminates some errors that often occurs • Added benefit: also makesprogramming easier

33. Why Not Always Use GC? • GC also has several drawbacks • Reachable objectscould, not will, be used again • More memory needed to hold the extra objects • It takes time to compute reachable objects live dead unreachable reachable

34. Why Not Always Use GC? • GC also has several drawbacks • Reachable objectscould, not will, be used again • More memory needed to hold the extra objects • It takes time to compute reachable objects live dead obj =new Object(); unreachable reachable free(obj)

35. can be explicitly freed Why Not Always Use GC? • GC also has several drawbacks • Reachable objectscould, not will, be used again • More memory needed to hold the extra objects • It takes time to compute reachable objects live dead obj =new Object(); unreachable reachable free(obj)

36. can be garbage collected Why Not Always Use GC? • GC also has several drawbacks • Reachable objectscould, not will, be used again • More memory needed to hold the extra objects • It takes time to compute reachable objects live dead unreachable reachable free(??)

37. Cost of Accessing Memory • How long memory access takes is also important • Will make a major difference in time program takes • Imaginary scenario usedto consider this effect:

38. Cost of Accessing Memory • How long memory access takes is also important • Will make a major difference in time program takes • Imaginary scenario usedto consider this effect: I want a beer

39. Registers and Caches • Inside the CPU, find first levels of memory • At the lowest level, are processor’s registers

40. Registers and Caches • Inside the CPU, find first levels of memory • At the lowest level, are processor’s registers

41. Registers and Caches • Inside the CPU, find first levels of memory • At the lowest level, are processor’s registers • Very, very fast but… • … number of beers held is limited

42. Registers and Caches • Inside the CPU, find first levels of memory • At the lowest level, are processor’s registers • Use caches at next level for dearest memory

43. Registers and Caches • Inside the CPU, find first levels of memory • At the lowest level, are processor’s registers • Use caches at next level for dearest memory

44. Registers and Caches • Inside the CPU, find first levels of memory • At the lowest level, are processor’s registers • Use caches at next level for dearest memory • More space than registers, but… • … not as fast (walk across room) • Will need more beer if party is good

45. Horrors! • Processor does its best to keep memory local • Caches organized to hold memory needed soon • Makes guesses, since this requires predicting future • Will eventually drink all beer in house

46. Horrors! • Processor does its best to keep memory local • Caches organized to hold memory needed soon • Makes guesses, since this requires predicting future • Will eventually drink all beer in house

47. Horrors! • Processor does its best to keep memory local • Caches organized to hold memory needed soon • Makes guesses, since this requires predicting future • Will eventually drink all beer in house • 12MB is largest cache size at the moment • Many programs need more than this • What do we do?

48. When the House Runs Dry… • What do you normally do when all beer gone? • Must go to store to get more… • … but do not want a DUI so we must walk to store • Processor uses RAM to store data that cannot fit • RAM sizes are much, much larger than caches • 100x slower to access, however

49. When Store Is Out Of Beer...

50. When Store Is Out Of Beer...