Concurrent key-value stores with range query support are crucial for the scalability and performance of many applications. Existing lock-free data structures of this kind use a fixed synchronization granularity. Using a fixed synchronization granularity in a concurrent key-value store with range query support is problematic as the best performing synchronization granularity depends on a number of factors that are difficult to predict, such as the level of contention and the number of items that are accessed by range queries. We present the first lock-free key-value store with linearizable range query support that dynamically adapts its synchronization granularity. This data structure is called the lock-free contention adapting search tree (LFCA tree). An LFCA tree does local adaptations of its synchronization granularity based on heuristics that take contention and the performance of range queries into account. We show that the operations of LFCA trees are linearizable, that the lookup operation is wait-free, and that the remaining operations (insert, remove and range query) are lock-free. Our experimental evaluation shows that LFCA trees achieve more than twice the throughput of related lock-free data structures in many scenarios. Furthermore, LFCA trees are able to perform substantially better than data structures with a fixed synchronization granularity over a wide range of scenarios due to their ability to adapt to the scenario at hand.

The scalability of parallel programs is often bounded by the performance of synchronization mechanisms used to protect critical sections. The performance of these mechanisms is in turn determined by their sequential execution time, efficient use of hardware, and ability to avoid waiting. In this article, we describe queue delegation (QD) locking, a family of locks that both delegate critical sections and enable detaching execution. Threads delegate work to the thread currently holding the lock and are able to detach, i.e., immediately continue their execution until they need a result from a previously delegated critical section. We show how to use queue delegation to build synchronization algorithms with lower overhead and higher throughput than existing algorithms, even when critical sections need to communicate results back immediately. Experiments when using up to 64 threads to access a shared priority queue show that QD locking provides 10 times higher throughput than Pthreads mutex locks and outperforms leading delegation algorithms. Also, when mixing parallel reads with delegated write operations, QD locking outperforms competing algorithms with an advantage ranging from 9.5 up to 207 percent increased throughput. Last but not least, continuing execution instead of waiting for the execution of critical sections leads to increased parallelism and better scalability. As we will see, queue delegation locking uses simple building blocks whose overhead is low even in uncontended use. All these make the technique useful in a wide variety of applications.

Distributed actor languages are an effective means of constructing scalable reliable systems, and the Erlang programming language has a well-established and influential model. While the Erlang model conceptually provides reliable scalability, it has some inherent scalability limits and these force developers to depart from the model at scale. This article establishes the scalability limits of Erlang systems and reports the work of the EU RELEASE project to improve the scalability and understandability of the Erlang reliable distributed actor model. We systematically study the scalability limits of Erlang and then address the issues at the virtual machine, language, and tool levels. More specifically: (1) We have evolved the Erlang virtual machine so that it can work effectively in large-scale single-host multicore and NUMA architectures. We have made important changes and architectural improvements to the widely used Erlang/OTP release. (2) We have designed and implemented Scalable Distributed (SD) Erlang libraries to address language-level scalability issues and provided and validated a set of semantics for the new language constructs. (3) To make large Erlang systems easier to deploy, monitor, and debug, we have developed and made open source releases of five complementary tools, some specific to SD Erlang. Throughout the article we use two case studies to investigate the capabilities of our new technologies and tools: a distributed hash table based Orbit calculation and Ant Colony Optimisation (ACO). Chaos Monkey experiments show that two versions of ACO survive random process failure and hence that SD Erlang preserves the Erlang reliability model. While we report measurements on a range of NUMA and cluster architectures, the key scalability experiments are conducted on the Athos cluster with 256 hosts (6,144 cores). Even for programs with no global recovery data to maintain, SD Erlang partitions the network to reduce network traffic and hence improves performance of the Orbit and ACO benchmarks above 80 hosts. ACO measurements show that maintaining global recovery data dramatically limits scalability; however, scalability is recovered by partitioning the recovery data. We exceed the established scalability limits of distributed Erlang, and do not reach the limits of SD Erlang for these benchmarks at this scale (256 hosts, 6,144 cores).

With multicourse being ubiquitous, concurrent data structures are becoming increasingly important. This paper proposes a novel approach to concurrent data structure design where the data structure collects statistics about contention and adapts dynamically according to this statistics. We use this approach to create a contention adapting binary search tree (CA tree) that can be used to implement concurrent ordered sets and maps. Our experimental evaluation shows that CA trees scale similar to recently proposed algorithms on a big multicore machine on various scenarios with a larger set size, and outperform the same data structures in more contended scenarios and in sequential performance. We also show that CA trees are well suited for optimization with hardware lock elision. In short, we propose a practically useful and easy to implement and show correct concurrent search tree that naturally adapts to the level of contention.

The scalability of parallel programs is often bounded by the performance of synchronization mechanisms used to protect critical sections. The performance of these mechanisms is in turn determined by their ability to use modern hardware efficiently and do useful work while or instead of waiting. This brief announcement sketches the idea and implementation of queue delegation locking, a synchronization mechanism that provides high throughput by allowing threads to efficiently delegate their critical sections to the thread currently holding the lock and by allowing threads that do not need a result from their critical section to continue executing immediately after delegating their work. Experiments show that queue delegation locking outperforms leading synchronization mechanisms due to the combination of its fast operation transfer with its ability to allow threads to continue doing useful work instead of waiting. Thanks to its simple building blocks, even its uncontended overhead is low, making queue delegation locking useful in a wide variety of applications.