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Ordered linked list implementation of a set By Lior Zibi abc - +

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1 Ordered linked list implementation of a set By Lior Zibi abc - +

2 Discussion Topics Defining the set and the linked list implementation. Some definitions. Algorithms: –1. Coarse grained synchronization. –2. Fine grained synchronization. –3. Optimistic synchronization. –4. Lazy synchronization.

3 Defining the linked list First, Set : Unordered collection of items No duplicates

4 Defining the linked list public interface Set { public boolean add(T x); public boolean remove(T x); public boolean contains(T x); } public class Node { public T item; public int key; public Node next; } Now the linked implements:

5 5 abc Sorted with Sentinel nodes (min & max possible keys) - + Defining the linked list

6 Defining concurrent methods properties Invariant: –Property that always holds. Established because –True when object is created. –Truth preserved by each method Each step of each method.

7 Defining concurrent methods properties Rep-Invariant: –The invariant on our concrete Representation = on the list. –Preserved by methods. –Relied on by methods. –Allows us to reason about each method in isolation without considering how they interact.

8 Defining concurrent methods properties Our Rep-invariant: –Sentinel nodes tail reachable from head. –Sorted –No duplicates Depends on the implementation.

9 Defining concurrent methods properties Abstraction Map: S(List) = –{ x | there exists a such that a reachable from head and a.item = x –} Depends on the implementation.

10 10 Abstract Data Types Example: –S( ) = {a,b} ab ab Concrete representation: Abstract Type: –{a, b}

11 Defining concurrent methods properties Wait-free: Every call to the function finishes in a finite number of steps. Supposing the Scheduler is fair: Starvation-free: every thread calling the method eventually returns.

12 Algorithms Next: going throw each algorithm. 1. Describing the algorithm. 2. Explaining why every step of the algorithm is needed. 3. Code review. 4. Analyzing each method properties. 5. Advantages / Disadvantages. 6. Presenting running times for my implementation of the algorithm. + Example of proving correctness for Remove(x) in FineGrained. Hopefully… Formal proofs of correctness lie beyond the scope of this book

13 13 0.Sequential List Based Set a c d a b c Add() Remove()

14 14 a c d b a b c Add() Remove() 0.Sequential List Based Set

15 15 1.Course Grained a b d 1. Describing the algorithm: Most common implementation today. Add(x) / Remove(x) / Contains(x): - Lock the entire list then perform the operation.

16 16 1.Course Grained a b d c Most common implementation today 1. Describing the algorithm: All methods perform operations on the list while holding the lock, so the execution is essentially sequential.

17 17 3. Code review: Add: public boolean add(T item) { Node pred, curr; int key = item.hashCode(); lock.lock(); try { pred = head; curr = pred.next; while (curr.key < key) { pred = curr; curr = curr.next; } if (key == curr.key) { return false; } else { Node node = new Node(item); node.next = curr; pred.next = node; return true; } } finally { lock.unlock(); } Finding the place to add the item Adding the item if it wasnt already in the list 1.Course Grained

18 Art of Multiprocessor Programming18 3. Code review: Remove: public boolean remove(T item) { Node pred, curr; int key = item.hashCode(); lock.lock(); try { pred = this.head; curr = pred.next; while (curr.key < key) { pred = curr; curr = curr.next; } if (key == curr.key) { pred.next = curr.next; return true; } else { return false; } } finally { lock.unlock(); } Finding the item Removing the item 1.Course Grained

19 19 3. Code review: Contains: public boolean contains(T item) { Node pred, curr; int key = item.hashCode(); lock.lock(); try { pred = head; curr = pred.next; while (curr.key < key) { pred = curr; curr = curr.next; } return (key == curr.key); } finally {lock.unlock(); } Finding the item Returning true if found 1.Course Grained

20 20 4. Methods properties: The implementation inherits its progress conditions from those of the Lock, and so assuming fair Scheduler: - If the Lock implementation is Starvation free Every thread will eventually get the lock and eventually the call to the function will return. So our implementation of Insert, Remove and Contains is Starvation-free 1.Course Grained

21 21 5. Advantages / Disadvantages: Advantages: - Simple. - Obviously correct. Disadvantages: - High Contention. (Queue locks help) - Bottleneck! 1.Course Grained

22 22 6. Running times: 1.Course Grained The tests were run on Aries – Supports 32 running threads. UltraSPARC T1 - Sun Fire T2000. Total of operations. 10% adds, 2% removes, 88% contains – normal work load percentages on a set. Each time the list was initialized with 100 elements. One run with a max of items in the list. Another with only 2000.

23 23 6. Running times: 1.Course Grained

24 24 2.Fine Grained 1. Describing the algorithm: Split object into pieces –Each piece has own lock. –Methods that work on disjoint pieces need not exclude each other.

25 25 2.Fine Grained Add(x) / Remove(x) / Contains(x): –Go throw the list, lock each node and release only after the lock of the next element has been acquired. –Once you have reached the right point of the list perform the Add / Remove / Contains operation. 1. Describing the algorithm:

26 26 abcd remove(b) 2.Fine Grained 1. Describing the algorithm: illustrated Remove.

27 27 abcd remove(b) 2.Fine Grained 1. Describing the algorithm: illustrated Remove.

28 28 abcd remove(b) 2.Fine Grained 1. Describing the algorithm: illustrated Remove.

29 29 abcd remove(b) 2.Fine Grained 1. Describing the algorithm: illustrated Remove.

30 30 abcd remove(b) 2.Fine Grained 1. Describing the algorithm: illustrated Remove.

31 31 acd remove(b) 2.Fine Grained 1. Describing the algorithm: illustrated Remove.

32 32 Why do we need to always hold 2 locks? 2.Fine Grained 2. Explaining why every step is needed.

33 33 abcd remove(c) remove(b) 2.Fine Grained Concurrent removes 2. Explaining why every step is needed.

34 34 abcd remove(b) remove(c) 2.Fine Grained Concurrent removes 2. Explaining why every step is needed.

35 35 abcd remove(b) remove(c) 2.Fine Grained Concurrent removes 2. Explaining why every step is needed.

36 36 abcd remove(b) remove(c) 2.Fine Grained Concurrent removes 2. Explaining why every step is needed.

37 37 abcd remove(b) remove(c) 2.Fine Grained Concurrent removes 2. Explaining why every step is needed.

38 38 abcd remove(b) remove(c) 2.Fine Grained Concurrent removes 2. Explaining why every step is needed.

39 39 Concurrent Removes abcd remove(b) remove(c) 2. Explaining why every step is needed.

40 40 Concurrent Removes abcd remove(b) remove(c) 2. Explaining why every step is needed.

41 41 acd remove(b) remove(c) 2.Fine Grained Concurrent removes 2. Explaining why every step is needed.

42 42 acd Bad news, C not removed remove(b) remove(c) 2.Fine Grained Concurrent removes 2. Explaining why every step is needed.

43 43 abcd remove(b) remove(c) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

44 44 abcd remove(b) remove(c) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

45 45 abcd remove(b) remove(c) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

46 46 abcd remove(b) remove(c) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

47 47 abcd remove(b) remove(c) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

48 48 abcd remove(b) remove(c) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

49 49 abcd remove(b) remove(c) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

50 50 abcd remove(b) remove(c) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

51 51 abcd Must acquire Lock of b remove(c) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

52 52 abcd Cannot acquire lock of b remove(c) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

53 53 abcd Wait! remove(c) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

54 54 abd Proceed to remove(b) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

55 55 abd remove(b) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

56 56 abd remove(b) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

57 57 ad remove(b) Concurrent removes Now with 2 locks. 2.Fine Grained 2. Explaining why every step is needed.

58 58 2.Fine Grained Conclusion: Now that we hold 2 locks for Remove / Add / Contains. If a node is locked : –It cant be removed and so does the next node in the list. –No new node can be added before it and after it. 2. Explaining why every step is needed.

59 59 3. Code review: Add: public boolean add(T item) { int key = item.hashCode(); head.lock(); Node pred = head; try { Node curr = pred.next; curr.lock(); try { while (curr.key < key) { pred.unlock(); pred = curr; curr = curr.next; curr.lock(); } Finding the place to add the item: 2.Fine Grained if (curr.key == key) { return false; } Node newNode = new Node(item); newNode.next = curr; pred.next = newNode; return true; } finally { curr.unlock(); } } finally { pred.unlock(); } Adding the item: Continued:

60 60 3. Code review: Remove: Node pred = null, curr = null; int key = item.hashCode(); head.lock(); try { pred = head; curr = pred.next; curr.lock(); try { while (curr.key < key) { pred.unlock(); pred = curr; curr = curr.next; curr.lock(); } 2.Fine Grained if (curr.key == key) { pred.next = curr.next; return true; } return false; } finally { curr.unlock(); } } finally { pred.unlock(); } Removing the item: Continued: Finding the place to add the item:

61 61 3. Code review: Contains: public boolean contains(T item) { Node pred = null, curr = null; int key = item.hashCode(); head.lock(); try { pred = head; curr = pred.next; curr.lock(); try { while (curr.key < key) { pred.unlock(); pred = curr; curr = curr.next; curr.lock(); } 2.Fine Grained return (curr.key == key); } finally { curr.unlock(); } } finally { pred.unlock(); } Return true iff found Continued: Finding the place to add the item:

62 62 Proving correctness for Remove(x) function: 2.Fine Grained So how do we prove correctness of a method in a concurrent environment? 1. Decide on a Rep-Invariant. Done! 2. Decide on an Abstraction map. Done! 3. Defining the operations: Remove(x): If x in the set => x wont be in the set and return true. If x isnt in the set => dont change the set and return false. Done!

63 63 Proving correctness for Remove(x) function: 2.Fine Grained 4. Proving that each function keeps the Rep-Invariant: 1. Tail reachable from head. 2. Sorted. 3. No duplicates. 1. The newly created empty list obviously keeps the Rep-invariant. 2. Easy to see from the code that for each function if the Rep-invariant was kept before the call it will still hold after it. Done!

64 64 Proving correctness for Remove(x) function: 2.Fine Grained 5. Split the function to all possible run time outcomes. In our case: 1. Successful remove. (x was in the list) 2. Failed remove.(x wasnt in the list) Done! 6. Proving for each possibility. We will start with a successful remove. (failed remove is not much different)

65 65 Proving correctness for Remove(x) function: 2.Fine Grained 6. Deciding on a linearization point for a successful remove. Reminder: Linearization point – a point in time that we can say the function has happened in a running execution. We will set the Linearization point to after the second lock was acquired.Done! successful remove.

66 66 Proving correctness for Remove(x) function: 2.Fine Grained Node pred = null, curr = null; int key = item.hashCode(); head.lock(); try { pred = head; curr = pred.next; curr.lock(); try { while (curr.key < key) { pred.unlock(); pred = curr; curr = curr.next; curr.lock(); } if (curr.key == key) { pred.next = curr.next; return true; } return false; } finally { curr.unlock(); } } finally { pred.unlock(); } In the code its: Here! Remove: Continued: successful remove.

67 67 Proving correctness for Remove(x) function: 2.Fine Grained 67 yzxw And in the list, for example: if the Set is {y,z,x,w} and we call Remove(x). After this lock is the linearization point: successful remove.

68 68 Proving correctness for Remove(x) function: 2.Fine Grained 7. Now that the linearization point is set we need to prove that: 7.1. Before the linearization point the set contained x After it the set wont contain x. successful remove.

69 69 Proving correctness for Remove(x) function: 2.Fine Grained 7.1. Before the linearization point the set contained x. 1. Since we proved the Rep-Invariant holds then pred=z is accessible from the head. 2. Since z,x are locked. No other parallel call can remove them. 3. Since curr=x is pointed to by pred then x is also accessible from the head. yzxw successful remove.

70 70 Proving correctness for Remove(x) function: 2.Fine Grained –S( ) = {a,b} yzxw 7.1. Before the linearization point the set contained x. 4. Now by the Abstraction map definition: since x is reachable from the head => x is in the set!Done! ab successful remove.

71 Proving correctness for Remove(x) function: 2.Fine Grained 7.1. After it the set wont contain x. 1. after the linearization point: pred.next = curr.next; Curr=x wont be pointed to by pred=z and so wont be accessible from head. 71 yzxw successful remove.

72 Proving correctness for Remove(x) function: 2.Fine Grained 7.1. After it the set wont contain x. 2. Now by the Abstraction map definition: since x is not reachable from the head => x is not in the set! Done! 72 yzxw successful remove.

73 Proving correctness for Remove(x) function: 2.Fine Grained 73 Now quicker for an unsuccessful remove: –Linearization point again will be after the second lock was acquired at: return false; –Before x wasnt in the list since pred is reachable from head & pred.key key. (x = key) and the rep-invariant property of an ordered list holds. –After the linearization point since it just returns false nothing changed in the Rep-Invariant and so nothing changed in the Abstraction-Map and the Set. 73 yzwv Done!

74 Proving correctness for Remove(x) function: 2.Fine Grained 74 In conclusion: –For every possible run time execution for Remove(x) we found a linearization point that holds the remove function specification in the set using the Abstraction map while holding the Rep-Invariant. Done!

75 75 4. Methods properties: 2.Fine Grained Assuming fair scheduler. If the Lock implementation is Starvation free: Every thread will eventually get the lock and since all methods move in the same direction in the list there wont be deadlock and eventually the call to the function will return. So our implementation of Insert, Remove and Contains is Starvation-free.

76 76 5. Advantages / Disadvantages: Advantages: - Better than coarse-grained lock Threads can traverse in parallel. Disadvantages: - Long chain of acquire/release. - Inefficient. 2.Fine Grained

77 77 6. Running times: 2.Fine Grained

78 78 6. Running times: 2.Fine Grained

79 79 6. Running times: 2.Fine Grained

80 80 3. Optimistic 1. Describing the algorithm: Add(x) / Remove (x) / Contains(x): 1. Find nodes without locking 2. Lock nodes 3. Check that everything is OK = Validation. 3.1 Check that pred is still reachable from head. 3.2 Check that pred still points to curr. 4. If validation passed => do the operation.

81 81 b d e a add(c) Aha! 3. Optimistic 1. Describing the algorithm: Example of add(c): Finding without locking

82 82 b d e a add(c) 1. Describing the algorithm: Locking Example of add(c): 3. Optimistic

83 83 b d e a add(c) 1. Describing the algorithm: Validation 1 Example of add(c): 3. Optimistic

84 84 b d e a add(c) 1. Describing the algorithm: Validation 1 Example of add(c): 3. Optimistic

85 85 b d e a add(c) 1. Describing the algorithm: Validation 2 Example of add(c): Yes. b is still reachable from head. 3. Optimistic

86 86 b d e a add(c) 1. Describing the algorithm: Validation 2 Example of add(c): Yes. b still points to d. 3. Optimistic

87 87 b d e a add(c) c 3. Optimistic 1. Describing the algorithm: Add c. Example of add(c):

88 88 Why do we need to Validate? 3. Optimistic 2. Explaining why every step is needed.

89 3. Optimistic First: Why do we need to validate that pred is accessible from head? Thread A Adds(c). After thread A found b, before A locks. Another thread removes b. 2. Explaining why every step is needed. b d e a

90 90 b d e a add(c) Aha! 3. Optimistic Adds(c). Finding without locking 2. Explaining why every step is needed.

91 91 d e a add(c) 3. Optimistic b Another thread removed b Adds(c). 2. Explaining why every step is needed.

92 92 d e a add(c) 3. Optimistic b Now A locks b and d Adds(c). 2. Explaining why every step is needed.

93 93 d e a add(c) 3. Optimistic b And adds c c Adds(c). 2. Explaining why every step is needed.

94 94 d e a 3. Optimistic b Now frees the locks. But c isnt added! c Adds(c). 2. Explaining why every step is needed.

95 3. Optimistic Second: Why do we need to validate that pred Still points to curr? Thread A removes(d). then thread A found b, before A locks. Another thread adds(c). 2. Explaining why every step is needed. b d e a

96 96 b d e a add(c) Aha! 3. Optimistic Removes(d) Finding without locking 2. Explaining why every step is needed.

97 97 d e a add(c) 3. Optimistic b Another thread Adds(c) c Removes(d) 2. Explaining why every step is needed.

98 98 d e a add(c) 3. Optimistic b Now A locks. c Removes(d) 2. Explaining why every step is needed.

99 99 d e a add(c) 3. Optimistic b pred.next = curr.next; Removes(d) c 2. Explaining why every step is needed.

100 Optimistic Instead c and d were deleted! Removes(d) d e a b c Now frees the locks. 2. Explaining why every step is needed.

101 Optimistic Do notice that threads might traverse deleted nodes. May cause problems to our Rep-Invariant. Careful not to recycle to the lists nodes that were deleted while threads are in a middle of an operation. With a garbage collection language like java – ok. For C – you need to solve this manually. Important comment.

102 Code review: Add: public boolean add(T item) { int key = item.hashCode(); while (true) { Entry pred = this.head; Entry curr = pred.next; while (curr.key <= key) { pred = curr; curr = curr.next; } pred.lock(); curr.lock(); Search the list from the beginning each time, until validation succeeds try { if (validate(pred, curr)) { if (curr.key == key) { return false; } else { Entry entry = new Entry(item); entry.next = curr; pred.next = entry; return true; } } finally { pred.unlock(); curr.unlock(); } If validation succeeds Attempt Add Continued: 3. Optimistic

103 Code review: Remove: public boolean add(T item) { int key = item.hashCode(); while (true) { Entry pred = this.head; Entry curr = pred.next; while (curr.key <= key) { pred = curr; curr = curr.next; } pred.lock(); curr.lock(); Search the list from the beginning each time, until validation succeeds try { if (validate(pred, curr)) { if (curr.key == key) { pred.next = curr.next; return true; } else { return false; } } finally { pred.unlock(); curr.unlock(); } If validation succeeds Attempt remove Continued: 3. Optimistic

104 Code review: Contains: public boolean contains(T item) { int key = item.hashCode(); while (true) { Entry pred = this.head; Entry curr = pred.next; while (curr.key < key) { pred = curr; curr = curr.next; } try { pred.lock(); curr.lock(); if (validate(pred, curr)) { return (curr.key == key); } } finally { pred.unlock(); curr.unlock(); } Search the list from the beginning each time, until validation succeeds If validation succeeds Return the result 3. Optimistic

105 Methods properties: Assuming fair scheduler. Even if all the lock implementations are Starvation free. We will show a scenario in which the methods Remove / Add / Contains do not return. And so our implementation wont be starvation free. 3. Optimistic

106 Methods properties: Assuming Thread A operation is Remove(d) / Add(c) / Contains(c). If the following sequence of operations will happen: 3. Optimistic d e a b

107 Methods properties: The sequence: 1. Thread A will find b. 2. Thread B will remove b. 3. The validation of thread A will fail. 3. Optimistic The thread call to the function wont return! d e a b 4. Thread C will add b. now go to 1.

108 Advantages / Disadvantages: Advantages: - Limited hot-spots Targets of add(), remove(), contains(). No contention on traversals. - Much less lock acquisition/releases. –Better concurrency. Disadvantages: - Need to traverse list twice! - Contains() method acquires locks. 2.Optimistic

109 Advantages / Disadvantages: 2.Optimistic Optimistic is effective if: –The cost of scanning twice without locks is less than the cost of scanning once with locks Drawback: –Contains() acquires locks. Normally, about 90% of the calls are contains.

110 Running times: 3. Optimistic

111 Running times: 3. Optimistic

112 Running times: 3. Optimistic

113 Lazy 1. Describing the algorithm: Validate: –Pred is not marked as deleted. –Curr is not marked as deleted. –Pred points to curr.

114 Lazy 1. Describing the algorithm: Remove(x): Find the node to remove. Lock pred and curr. Validate. (New validation!) Logical delete –Marks current node as removed (new!). Physical delete –Redirects predecessors next.

115 Lazy 1. Describing the algorithm: Add(x): Find the node to remove. Lock pred and curr. Validate. (New validation!) Physical add –The same as Optimistic.

116 Lazy 1. Describing the algorithm: Contains(x): Find the node to remove without locking! Return true if found the node and it isnt marked as deleted. No locks!

117 117 aa b c d 4. Lazy 1. Describing the algorithm: Remove(c):

118 c 118 aa b d Present in list 4. Lazy 1. Describing the algorithm: Remove(c): 1. Find the node

119 c 119 aa b d Present in list 4. Lazy 1. Describing the algorithm: Remove(c): 2. lock

120 c 120 aa b d Present in list 4. Lazy 1. Describing the algorithm: Remove(c): 3. Validate

121 c 121 aa b d Set as marked 4. Lazy 1. Describing the algorithm: Remove(c): 4. Logically delete

122 122 aa b c d Pred.next = curr.next 4. Lazy 1. Describing the algorithm: Remove(c): 5. Physically delete

123 123 aa b d Cleaned 4. Lazy 1. Describing the algorithm: Remove(c): 5. Physically delete

124 Lazy 1. Describing the algorithm: Given the Lazy Synchronization algorithm. What else should we change?

125 Lazy 1. Describing the algorithm: New Abstraction map! S(head) = –{ x | there exists node a such that a reachable from head and a.item = x and a is unmarked –}

126 126 Why do we need to Validate? 2. Explaining why every step is needed. 4. Lazy

127 First: Why do we need to validate that pred Still points to curr? The same as in Optimistic: Thread A removes(d). Then thread A found b, before A locks. Another thread adds(c). –c and d will be removed instead of just d. 2. Explaining why every step is needed. 4. Lazy b d e a

128 Second: Why do we need to validate that pred and curr arent marked logically removed? To make sure a thread hasnt removed them between our find and our lock. The same scenario we showed for validating that pred is still accessible from head holds here: –After thread A found b, before A locks. Another thread removes b. (our operation wont take place). 2. Explaining why every step is needed. 4. Lazy d e b a

129 Code review: Add: public boolean add(T item) { int key = item.hashCode(); while (true) { Node pred = this.head; Node curr = head.next; while (curr.key < key) { pred = curr; curr = curr.next; } pred.lock(); try { curr.lock(); Search the list from the beginning each time, until validation succeeds try { if (validate(pred, curr)) { if (curr.key == key) { return false; } else { Node Node = new Node(item); Node.next = curr; pred.next = Node; return true; } } finally { curr.unlock(); } } finally { pred.unlock(); } If validation succeeds Attempt Add Continued: 4. Lazy

130 Code review: Remove: public boolean remove(T item) { int key = item.hashCode(); while (true) { Node pred = this.head; Node curr = head.next; while (curr.key < key) { pred = curr; curr = curr.next; } pred.lock(); try { curr.lock(); try { Search the list from the beginning each time, until validation succeeds try{ if (validate(pred, curr)) { if (curr.key != key) { return false; } else { curr.marked = true; pred.next = curr.next; return true; } } finally { curr.unlock(); } } finally { pred.unlock(); } Validation Continued: 4. Lazy Logically remove Physically remove

131 Code review: Contains: public boolean contains(T item) { int key = item.hashCode(); Node curr = this.head; while (curr.key < key) curr = curr.next; return curr.key == key && !curr.marked; } Check if its there and not marked 4. Lazy No Lock!

132 Methods properties: Remove and Add: Assuming fair scheduler. Even if all the lock implementations are Starvation free. The same scenario we showed for optimistic holds here. (only here the validation will fail because the node will be marked and not because it cant be reached from head) And so our implementation wont be starvation free. 4. Lazy

133 Methods properties: But… Contains: Contains does not lock! In fact it isnt dependent on other threads to work. And so… Contains is Wait-free. Do notice that other threads cant increase the list forever while the thread is in contains because we have a maximum size to the list (

134 Advantages / Disadvantages: Advantages: –Contains is Wait-free. Usually 90% of the calls! –Validation doesnt rescan the list. Drawbacks: –Failure to validate restarts the function call. –Add and Remove use locks. Next implementation: (not today) Lock free list. 4. Lazy

135 Running times: 4. Lazy

136 Running times: 4. Lazy

137 Running times: 4. Lazy

138 Art of Multiprocessor Programming138 Summary Our winner: Lazy. Second best: Optimistic. Third:Fine-Grained. Last:Coarse-Grained. ?

139 Art of Multiprocessor Programming139 Summary Answer: No. Choose your implementation carefully based on your requirements.

140 140 The end


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