We demonstrate that general-purpose memory allocation involving many threads on many cores can be done with high performance, multicore scalability, and low memory consumption. For this purpose, we have designed and implemented scalloc, a concurrent allocator that generally performs and scales in our experiments better than other allocators while using less memory, and is still competitive otherwise. The main ideas behind the design of scalloc are: uniform treatment of small and big objects through so-called virtual spans, efficiently and effectively reclaiming free memory through fast and scalable global data structures, and constant-time (modulo synchronization) allocation and deallocation operations that trade off memory reuse and spatial locality without being subject to false sharing.
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@cite_28: Lock-freedom is a progress guarantee that ensures overall program progress. Wait-freedom is a stronger progress guarantee that ensures the progress of each thread in the program. While many practical lock-free algorithms exist, wait-free algorithms are typically inefficient and hardly used in practice. In this paper, we propose a methodology called fast-path-slow-path for creating efficient wait-free algorithms. The idea is to execute the efficient lock-free version most of the time and revert to the wait-free version only when things go wrong. The generality and effectiveness of this methodology is demonstrated by two examples. In this paper, we apply this idea to a recent construction of a wait-free queue, bringing the wait-free implementation to perform in practice as efficient as the lock-free implementation. In another work, the fast-path-slow-path methodology has been used for (dramatically) improving the performance of a wait-free linked-list.
@cite_15: There is a trade-off between performance and correctness in implementing concurrent data structures. Better performance may be achieved at the expense of relaxing correctness, by redefining the semantics of data structures. We address such a redefinition of data structure semantics and present a systematic and formal framework for obtaining new data structures by quantitatively relaxing existing ones. We view a data structure as a sequential specification S containing all "legal" sequences over an alphabet of method calls. Relaxing the data structure corresponds to defining a distance from any sequence over the alphabet to the sequential specification: the k-relaxed sequential specification contains all sequences over the alphabet within distance k from the original specification. In contrast to other existing work, our relaxations are semantic (distance in terms of data structure states). As an instantiation of our framework, we present two simple yet generic relaxation schemes, called out-of-order and stuttering relaxation, along with several ways of computing distances. We show that the out-of-order relaxation, when further instantiated to stacks, queues, and priority queues, amounts to tolerating bounded out-of-order behavior, which cannot be captured by a purely syntactic relaxation (distance in terms of sequence manipulation, e.g. edit distance). We give concurrent implementations of relaxed data structures and demonstrate that bounded relaxations provide the means for trading correctness for performance in a controlled way. The relaxations are monotonic which further highlights the trade-off: increasing k increases the number of permitted sequences, which as we demonstrate can lead to better performance. Finally, since a relaxed stack or queue also implements a pool, we actually have new concurrent pool implementations that outperform the state-of-the-art ones.
@cite_16: A prominent remedy to multicore scalability issues in concurrent data structure implementations is to relax the sequential specification of the data structure. We present distributed queues (DQ), a new family of relaxed concurrent queue implementations. DQs implement relaxed queues with linearizable emptiness check and either configurable or bounded out-of-order behavior or pool behavior. Our experiments show that DQs outperform and outscale in micro- and macrobenchmarks all strict and relaxed queue as well as pool implementations that we considered.
@cite_25: Computer architecture is about to undergo, if not another revolution, then a vigorous shaking-up. The major chip manufacturers have, for the time being, simply given up trying to make processors run faster. Instead, they have recently started shipping "multicore" architectures, in which multiple processors (cores) communicate directly through shared hardware caches, providing increased concurrency instead of increased clock speed.As a result, system designers and software engineers can no longer rely on increasing clock speed to hide software bloat. Instead, they must somehow learn to make effective use of increasing parallelism. This adaptation will not be easy. Conventional synchronization techniques based on locks and conditions are unlikely to be effective in such a demanding environment. Coarse-grained locks, which protect relatively large amounts of data, do not scale, and fine-grained locks introduce substantial software engineering problem.Transactional memory is a computational model in which threads synchronize by optimistic, lock-free transactions. This synchronization model promises to alleviate many (not all) of the problems associated with locking, and there is a growing community of researchers working on both software and hardware support for this approach. This talk will survey the area, with a focus on open research problems.
@cite_11: Linearizability, the key correctness condition that most optimized concurrent object implementations comply with, imposes tight synchronization between the object concurrent operations. This tight synchronization usually comes with a performance and scalability price. Yet, these implementations are often employed in an environment where a more relaxed linearizability condition suffices, where strict linearizability is not a must. Here we provide a quantitative definition of limited non-determinism, a notion we call Quasi Linearizability. Roughly speaking an implementation of an object is quasi linearizable if each run of the implementation is at a bounded "distance" away from some linear run of the object. However, as we show the limited distance has to be relative to some operations but not all. Following the definition we provide examples of quasi concurrent implementations that out perform state of the art standard implementations due to the relaxed requirement. Finally we show that the Bitonic Counting Network non-deterministic behavior can be quantified using our Quasi Linearizable notion.
Concurrent data structures in the fast path of the frontend as well as the backend of scalloc are lock-free @cite_25 . A recent trend towards semantically relaxed concurrent data structures @cite_11 @cite_14 opens doors for new design ideas and even greater performance and scalability so that hierarchies of spans (buffers in general) can be avoided and may be utilized globally across the whole system. The concurrent data structures in scalloc are pools, allowing in principle every data structure with pool semantics to be used. However, unlike segment queues @cite_11 and @math -Stacks @cite_15 , Distributed Queues @cite_16 with Treiber stacks @cite_22 , as used in scalloc, do not require dynamically allocated administrative memory (such as sentinel nodes), which is important for building efficient memory allocators. The data structures for reusable spans within a TLAB are implemented using locks but could in principle be replaced with wait-free sets, which nowadays can be implemented almost as efficiently as their lock-free counterparts @cite_28 .