A method for implementing lock-free shared data structures

We are interested in implementing data structures on shared memory multiprocessors. A natural model for these machines is an asynchronous parallel machine, in which the processors are subject to arbitrary delays. On such machines, it is desirable for algorithms to be {\em lock-free}, that is, they must allow concurrent access to data without using mutual exclusion. Efficient lock-free implementations are known for some specific data structures, but these algorithms do not generalize well to other structures. For most data structures, the only previously known lock-free algorithm is due to Herlihy. Herlihy presents a simple methodology to create a lock-free implementation of a general data structure, but his approach can be very expensive. We present a technique that provides the semantics of exclusive access to data without using mutual exclusion. Using this technique, we devise the {\em caching method}, a general method of implementing lock-free data structures that is provably better than Herlihy's methodology for many well-known data structures. The cost of one operation using the caching method is proportional to $T \log T$, where $T$ is the sequential cost of the operation. Under Herlihy's methodology, the cost is proportional to $T + C$, where $C$ is the time needed to make a logical copy of the data structure. For many data structures, such as arrays and {\em well connected} pointer-based structures (e.g., a doubly linked list), the best known value for $C$ is proportional to the size of the structure, making the copying time much larger than the sequential cost of an operation. The new method can also allow {\em concurrent updates} to the data structure; Herlihy's methodology cannot. A correct lock-free implementation can be derived from a correct sequential implementation in a straightforward manner using this method. The method is also flexible; a programmer can change many of the details of the default implementation to optimize for a particular pattern of data structure use

Easier Parallel Programming with Provably-Efficient Runtime Schedulers

Over the past decade processor manufacturers have pivoted from increasing uniprocessor performance to multicore architectures. However, utilizing this computational power has proved challenging for software developers. Many concurrency platforms and languages have emerged to address parallel programming challenges, yet writing correct and performant parallel code retains a reputation of being one of the hardest tasks a programmer can undertake. This dissertation will study how runtime scheduling systems can be used to make parallel programming easier. We address the difficulty in writing parallel data structures, automatically finding shared memory bugs, and reproducing non-deterministic synchronization bugs. Each of the systems presented depends on a novel runtime system which provides strong theoretical performance guarantees and performs well in practice