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regions, they d o so with different monitor objects: 5_1 o c kX and 5_1oc kY The result is that both methods run fully concurrent with one another, meaning that g may read state updates being made to 5_X by method f before they are complete Even if all g is doing is reading from the object, there could be some invariant protecting the relationship between fields f1 and f2 of Data instances And observing the broken invariants could lead to g crashing One of the most widely known dynamic race condition detection algo rithms, called the lock set algorithm, popularized by several research sys tems such as Eraser (see Further Reading, Savage, Burrows, Nelson, Sobalvarro) and RaceTrack (see Further Reading, Yu, Rodeheffer, Chen) looks for these kinds of inconsistent data protection races They even try to determine when a race is benign (ie, all shared accesses are reads) or a potential disaster An in-depth analysis of the algorithm itself is outside of the scope of this book, though interested readers might want to read more about it The basic idea is as follows: the system monitors all critical regions in the program and which memory locations are accessed under the pro tection of these critical regions during execution of the program The algo rithm uses this information to continuously refine its guess as to which locks are candidates for protecting particular memory locations It does so by taking the intersection of all locks held by a thread whenever a particu lar location was accessed In our above example, if one thread executed f first, the candidate set is { s_loc kX }; when g runs, it also gets a candidate set This set is { 5_1 o c kY }, which, when intersected with the previous set

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mine that there's a bug in the program shown There have been other recent approaches to solving this problem, includ ing static race condition detection For example, Abadi, et al (see Further Reading) proposed language extensions to associate locks with fields and to check that whenever a particular field was accessed the associated lock was held by the current thread Neither dynamic nor static race condition detection is broadly available in tools on the Windows platform today

A classic tradeoff when it comes to synchronization is critical region granu larity There is a constant tension between fine granularity-which generally gives better scalability, worse single-threaded performance (due to more lock

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We used the process of multiplying the series T = 1+q+2q2 +3q3 +4q4 + by q in order to sum the series because this process will occur again We could also have noted that T = 1/(1 q)2 = (1 q) 2 = 1/p2 by the binomial expansion with

Unemployment Insurance / 187

acquisitions), and results i n far subtler and deadlock prone code-and coarse granul arity which generally gives superior single threaded performance,

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errs on the side of simplicity and correctness, but sacrifices scalability But the tension to make critical sections as fine as possible can sometimes lead to accidentally releasing them too soon This can expose broken invariants to other threads It is imperative that critical regions span the entire sequence of opera tions that make up some larger composite action We've already covered serializability and Iinearizability, where some program action comprised of multiple steps is meant to appear as an atomic, indivisible action For this to be achieved, the entire action must be wrapped in a critical region such that when it is released all invariants hold The tension between perform ance and scaling can lead programmers to overtighten the granularity of a lock or to sneak in a few reads without using synchronization, thus introducing a general race condition As an example of where an overly fine-grained lock can break your pro gram, imagine we are using a lock to protect access to a simple linked list We want to remove the head node This entails multiple synchronization sensitive reads and writes: first, we must read the head node; then we have to read the head's current next node; and, finally, we must store a reference to the old head's next node to the head variable That's two reads and a sin gle write; if we don't protect all of them by the same critical region, another thread could sneak in and change the data, causing us trouble Here's an incorrectly synchronized version of this algorithm

c l a s s LinkedSt a c k < T > c l a s s L i stNode { i n t e r n a l T m_va l u e j i n t e r n a l L i stNode m_next j } p rivate obj e c t m_loc k

a negative exponent We will generalize this in the next section and see that our last two examples are special cases of what we will call the negative binomial distribution

.

p r ivate L i stNode m_head p u b l i c T Pop ( ) {

II Avoid syn c h ronizat ion if t h e l i st is empty L i stNode c u rrHead m_hea d j II Read t h e head o n c e

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