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The G et B u c ketAn d Loc kNo is then used in various places when the appropriate indices into m_b u c k e t s and m_l o c k s are needed It is imple mented simply with modulus: the hash code is taken from the key, and we modulus it with the bucket count, giving us b u c ket No; then we modulus the b u c ketNo with the lock count, giving us l o c kNo This method also validates that the key provided is not n u l l : supporting n u l l keys could be done by treating them like as When Ad d is called, it computes these indices and then allocates a new node It takes the lock using its loc kNo index as late as possible and pushes the new node on the front of the linked list in the appropriate bucket We could have reasonably added it to the tail (LIFO order versus FIFO), but this could incur an O(N) traversal of the bucket list It's also worth pointing out that we might have considered a lock free stack for the buckets but that doing so would cause some issues when it comes to removing elements (since the lock free stack doesn't support random access) Some lock free hashtable algorithms use a lock free linked list to support the random access requirements The Remove method works similar to Add, with one interesting caveat: it checks the bucket for a n u l l value (meaning it is empty) before even acquir ing a lock This is a minor optimization-and a questionable one-but is shown for illustration purposes only Finally, the l ryGet and indexer methods do not acquire locks at all The reason this works is subtle The linearization point for adding a new ele ment is the write to the appropriate bucket that links on a new node; and the point for removing an element is the write to the appropriate bucket or node's next pointer Notice that the linearization point is not when the lock is released inside Add or Remove; this is an important distinction to make, because if the hash table ever required more complicated invariants that could not be captured in a single atomic write, then the lock free reading would not work For this to function properly, writes must also retire in order (which is guaranteed by the NET memory model) so that a node can not be seen with an empty key or value Additionally, the lock free reads must occur in order too: this is accomplished by issuing an explicit Memo ry.

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(see page 13 of the instructions)

We'll only review a few lock free data structures There is a wealth of literature on building lock free linked lists, sets, hash tables, and the like-- this is an area of increasingly active and ongoing research-and the aim of this book is not to present a comprehensive overview of all of them Rather, we will see a couple illustrative examples that, coupled with the contents of 1 0, Memory Models and Lock Freedom, will enable you to learn more about and experiment with the current state of the art

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General-Purpose Lock Free F I FO Queue There is a straightforward lock free queue algorithm that was popularized by Michael and Scott (see Further Reading, 1 996) about a decade ago It is somewhat similar to the fine-grained queue we saw earlier, and is effec tively an extension of the lock free stack algorithm we already looked at in 1 0, Memory Models and Lock Freedom: nodes are the same struc ture, but in addition to a head reference, we also maintain a tail reference too Enqueuing a new node places it at the tail end, and dequeuing removes from the head end There is some subtlety around how we ensure both the head and tail pointers, plus all the next pointers in the linked chain, stay in sync This will be explained in more detail after seeing the code Here is an implementation of a Loc k F reeQu e u e < T > class

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u s ing u s i ng u s i ng using System j System Co l l e c t i o n s j System Co l l e c t i o n s Ge n e ri c j System Th read i n g j

Foreign taxes paid or accrued In US dollars (s) Other foreign taxes paid or accrued Taxes withheld at source on: (t) Dividends (u) Rents and royalties (v) Interest (w) Other foreign taxes paid or accrued (x) Total foreign taxes paid or accrued (add cols (t) through (w))

#pragma wa r n i n g d i s a ble a42a p u b l i c c l a s s Loc k F reeQueue< T > { c l a s s Node i n t e r n a l T m_va l j i n t e r n a l volat i l e Node m_next j } p r ivate volat i l e Node m_head j p r ivate vol a t i l e Node m_t a i l j p u b l i c Con c u r rentQu e u e ( ) I E n u me r a b l e < T >

It is more likely to nd a sum near 1 than to nd a sum near either of the extreme values, namely, 0 or 2, as we shall see It can be shown that the probability distribution of X = X1 + X2 is f2 (x) = x for 0 x 1 2 x for 1 x 2

{ } p u b l i c int count { get { int count = 0 j for ( Node c u r r = m_head m_next j c u r r ! = n U l l j c u r r = c u rr m_next ) count++j ret u r n count j }

p u b l i c bool I s Empty { nullj } } p rivate Node Get T a i lAndCat c h Up ( ) { Node t a i l Node n e x t m_t a i l j t a i l m_next j

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