Concurrent data structures and actor programming under the Matroshka model

In this paper we introduce Range Query Timestamp Counter (RQ-TSC), a general approach to provide scalable and linearizable range query operations for highly concurrent lock-based data structures. RQ-TSC is a multi-versioned building block that relies on hardware timestamps (e.g., obtained through hardware timestamp counter register on x86_64) to generate version timestamps, which greatly reduce a point of contention on a shared atomic counter. To evaluate the performance of RQ-TSC, we apply it to three data structures: a linked list, a skip list, and a binary search tree. Experiments show that our approach can improve scalability significantly. Moreover, in almost all cases, range queries on these data structures built from our design perform as well as or better than state-of-the-art concurrent data structures that support linearizable range queries.

Memory contention can be a serious performance bottleneck in concurrent programs on shared-memory multicore architectures. Having all threads write to a small set of shared locations, for example, can lead to orders of magnitude loss in performance relative to all threads writing to distinct locations, or even relative to a single thread doing all the writes. Shared write access, however, can be very useful in parallel algorithms, concurrent data structures, and protocols for communicating among threads.We study the operation as a useful primitive for limiting write contention in parallel and concurrent programs. A priority update takes as arguments a memory location, a new value, and a comparison function >p that enforces a partial order over values. The operation atomically compares the new value with the current value in the memory location, and writes the new value only if it has higher priority according to >p. On the implementation side, we show that if implemented appropriately, priority updates greatly reduce memory contention over standard writes or other atomic operations when locations have a high degree of sharing. This is shown both experimentally and theoretically. On the application side, we describe several uses of priority updates for implementing parallel algorithms and concurrent data structures, often in a way that is deterministic, guarantees progress, and avoids serial bottlenecks. We present experiments showing that a variety of such algorithms and data structures perform well under high degrees of sharing. Given the results, we believe that the priority update operation serves as a useful parallel primitive and good programming abstraction as (1) the user largely need not worry about the degree of sharing, (2) it can be used to avoid non-determinism since, in the common case when >p is a total order, priority updates commute, and (3) it has many applications to programs using shared data.

Memory contention can be a serious performance bottleneck in concurrent programs on shared-memory multicore architectures. Having all threads write to a small set of shared locations, for example, can lead to orders of magnitude loss in performance relative to all threads writing to distinct locations, or even relative to a single thread doing all the writes. Shared write access, however, can be very useful in parallel algorithms, concurrent data structures, and protocols for communicating among threads. We study the \priority operation as a useful primitive for limiting write contention in parallel and concurrent programs. A priority update takes as arguments a memory location, a new value, and a comparison function >p that enforces a partial order over values. The operation atomically compares the new value with the current value in the memory location, and writes the new value only if it has higher priority according to >p. On the implementation side, we show that if implemented appropriately, priority updates greatly reduce memory contention over standard writes or other atomic operations when locations have a high degree of sharing. This is shown both experimentally and theoretically. On the application side, we describe several uses of priority updates for implementing parallel algorithms and concurrent data structures, often in a way that is deterministic, guarantees progress, and avoids serial bottlenecks. We present experiments showing that a variety of such algorithms and data structures perform well under high degrees of sharing. Given the results, we believe that the priority update operation serves as a useful parallel primitive and good programming abstraction as (1) the user largely need not worry about the degree of sharing, (2) it can be used to avoid non-determinism since, in the common case when >p is a total order, priority updates commute, and (3) it has many applications to programs using shared data.

We introduce OPTIK, a new practical design pattern for designing and implementing fast and scalable concurrent data structures. OPTIK relies on the commonly-used technique of version numbers for detecting conflicting concurrent operations. We show how to implement the OPTIK pattern using the novel concept of OPTIK locks. These locks enable the use of version numbers for implementing very efficient optimistic concurrent data structures. Existing state-of-the-art lock-based data structures acquire the lock and then check for conflicts. In contrast, with OPTIK locks, we merge the lock acquisition with the detection of conflicting concurrency in a single atomic step, similarly to lock-free algorithms. We illustrate the power of our OPTIK pattern and its implementation by introducing four new algorithms and by optimizing four state-of-the-art algorithms for linked lists, skip lists, hash tables, and queues. Our results show that concurrent data structures built using OPTIK are more scalable than the state of the art.

Although concurrent data structures are commonly used in practice on shared-memory machines, even the most efficient concurrent structures often lack performance theorems guaranteeing linear speedup for the enclosing parallel program. Moreover, efficient concurrent data structures are difficult to design. In contrast, parallel batched data structures do provide provable performance guarantees, since processing a batch in parallel is easier than dealing with the arbitrary asynchrony of concurrent accesses. They can limit programmability, however, since restructuring a parallel program to use batched data structure instead of concurrent data structure can often be difficult or even infeasible. This paper presents BATCHER, a scheduler that achieves the best of both worlds through the idea of implicit batching, and a corresponding general performance theorem. BATCHER takes as input (1) a dynamically multithreaded program that makes arbitrary parallel accesses to an abstract data type, and (2) an implementation of the abstract data type as a batched data structure that need not cope with concurrent accesses. BATCHER extends a randomized work-stealing scheduler and guarantees probably good performance to parallel algorithms that use these data structures. In particular, suppose a parallel algorithm has (i)T_1(i/) work, (I)T_∞(I/) span, and (I)n(I/) data-structure operations. Let (I)W(n)(I/) be the total work of data-structure operations and let (I)s(n)(I/) be the span of a size-(I)P(I/) batch. Then BATCHER executes the program in (I)O((T_1+W(n) + n s(n))/P+ s(n) T_∞)(I/) expected time on (I)P(I/) processors. For higher-cost data structures like search trees and large enough (I)n(I/), this bound becomes (I)(T_1+n\lg n)/P + T_∞lg n)(I/) provably matching the work of a sequential search tree but with nearly linear speedup, even though the data structure is accessed concurrently. The BATCHER runtime bound also readily extends to data structures with amortized bounds.

Concurrent data structures provide the means to multi-threaded applications to share data. Typical designs of concurrent data structures are based on locks in order to avoid inconsistency due to concurrent modifications. Locks though introduce a sequential component in Amdahl's law. Lock-free algorithmic designs of concurrent data structures were introduced in the quest for better performance and scalability and are widely used in practice. Lock-free designs typically employ optimistic conflict control making performance analysis challenging. In this talk, I will describe recent efforts in analyzing their performance.

Design of an efficient thread-safe concurrent data structure is a balancing act between its implementation complexity and performance. Lock-based concurrent data structures, which are relatively easy to derive from their sequential counterparts and to prove thread-safe, suffer from poor throughput under even light multi-threaded workload. At the same time, lock-free concurrent structures allow for high throughput, but are notoriously difficult to get right and require careful reasoning to formally establish their correctness. In this work, we explore a solution to this conundrum based on a relatively old idea of batch parallelism---an approach for designing high-throughput concurrent data structures via a simple insight: efficiently processing a batch of a priori known operations in parallel is easier than optimising performance for a stream of arbitrary asynchronous requests. Alas, batch-parallel structures have not seen wide practical adoption due to (i) the inconvenience of having to structure multi-threaded programs to explicitly group operations and (ii) the lack of a systematic methodology to implement batch-parallel structures as simply as lock-based ones. We present OBatcher---a Multicore OCaml library that streamlines the design, implementation, and usage of batch-parallel structures. OBatcher solves the first challenge (how to use) by suggesting a new lightweight implicit batching design pattern that is built on top of generic asynchronous programming mechanisms. The second challenge (how to implement) is addressed by identifying a family of strategies for converting common sequential structures into the corresponding efficient batch-parallel versions, and by providing a library of functors that embody those strategies. We showcase OBatcher with a diverse set of benchmarks ranging from Red-Black and AVL trees to van Emde Boas trees, skip lists, and a thread-safe implementation of a Datalog solver. Our evaluation of all the implementations on large asynchronous workloads shows that (a) they consistently outperform the corresponding coarse-grained lock-based implementations---the only ones available in OCaml to date, and that (b) their throughput scales reasonably with the number of processors.

We introduce OPTIK, a new practical design pattern for designing and implementing fast and scalable concurrent data structures. OPTIK relies on the commonly-used technique of version numbers for detecting conflicting concurrent operations. We show how to implement the OPTIK pattern using the novel concept of OPTIK locks. These locks enable the use of version numbers for implementing very efficient optimistic concurrent data structures. Existing state-of-the-art lock-based data structures acquire the lock and then check for conflicts. In contrast, with OPTIK locks, we merge the lock acquisition with the detection of conflicting concurrency in a single atomic step, similarly to lock-free algorithms. We illustrate the power of our OPTIK pattern and its implementation by introducing four new algorithms and by optimizing four state-of-the-art algorithms for linked lists, skip lists, hash tables, and queues. Our results show that concurrent data structures built using OPTIK are more scalable than the state of the art.

Nowadays, modern multicore embedded systems often execute complex applications that rely heavily on concurrent data structures. Databases on embedded microservers, file systems and stream processing algorithms belong in application domains that normally utilize concurrent data structures to store and process their data. The prevalent lock-based synchronization methods based on mutexes provide poor scalability and, most importantly, they lead to high energy consumption, which is an important constraint on embedded systems. In this work, we propose an energy efficient synchronization model for embedded system architectures based on message-passing communication. Our results show that concurrent data structures based on the proposed model provide lower power consumption in comparison with the corresponding lock-based implementations, along with comparable performance.

Concurrent data structures provide the means to multi-threaded applications to share data. Data structures come with a set of predefined operations, specified by the semantics of the data structure. In the literature and in several contemporary commonly used programming environments, the notion of iteration has been introduced for collection data structures, as a bulk operation enhancing the native set of operations. Iterations in several of these contexts have been treated as sequential in nature and may provide weak consistency guarantees when running concurrently with the native operations of the data structures. In this work we study iterations in concurrent data structures in the context of concurrency with the native operations and the guarantees that they provide. Besides invariability, we propose a set of consistency specifications for such bulk operations, including also concurrency-aware properties by building on Lamppost's systematic definitions for registers. Furthermore, by using queues and composite registers as case-studies of underlying objects, we provide a set of constructions of iteration operations, satisfying the properties and showing containment relations. Besides the trade-off between consistency and throughput, we point out and study trade-off between the overhead of the bulk operation and possible support (helping) by the native operations of the data structure.

The irruption of chiplet-based architectures has been a game changer, enabling higher transistor integration and core counts in a single socket. However, chiplets impose higher and non-uniform memory access (NUMA) latencies than monolithic integration. This harms the efficiency of atomic memory operations (AMOs), which are fundamental to implementing fine-grained synchronization and concurrent data structures on large systems. AMOs are executed either near the core (near) or at a remote location within the cache hierarchy (far). On near AMOs, the core’s private cache fetches the target cache line in exclusiveness to modify it locally. Near AMOs cause significant data movement between private caches, especially harming parallel applications’ performance on chiplet-based architectures. Alternatively, far AMOs can alleviate the communication overhead by reducing data movement between processing elements. However, current multicore architectures only support one type of far AMO, which sends all updates to a single serialization point (centralized AMOs). This work introduces two new types of far AMOs, delegated and migrating, that execute AMOs remotely without centralizing updates in a single point of the cache hierarchy. Combining centralized, delegated, and migrating AMOs allows the directory to select the best location to execute AMOs. Moreover, we propose Delegato, a tracing optimization to effectively transport usage information from private caches to the directory to predict the best atomic type to issue accurately. Additionally, we design a simple predictor on top of Delegato that seamlessly selects the best placement to perform AMOs based on the data access pattern and usage activity of cores. Our evaluation using gem5 shows that Delegato can speed up applications on average by 1.07 × over centralized AMOs and by 1.13 × over the state-of-the-art AMO predictor.

Concurrent data structures often provide better performance on multi-core processors but are significantly more difficult to design and test than their sequential counterparts. The C/C++11 standard introduced a weak memory model with support for low-level atomic operations such as compare and swap (CAS). While low-level atomic operations can significantly improve the performance of concurrent data structures, they introduce non-intuitive behaviors that can increase the difficulty of developing code.

Technologies like RDMA and Gen-Z, which give access to memory outside the box, are gaining in popularity. These technologies provide the abstraction of far memory, where memory is attached to the network and can be accessed by remote processors without mediation by a local processor. Unfortunately, far memory is hard to use because existing data structures are mismatched to it. We argue that we need new data structures for far memory, borrowing techniques from concurrent data structures and distributed systems. We examine the requirements of these data structures and show how to realize them using simple hardware extensions.

In this paper we study the problem of designing searchable concurrent data structures with performance guarantees that can be used in a distributed environment where data elements are stored in a dynamically changing set of nodes. Searchable data structures are data structures that provide three basic operations: INSERT, DELETE, and SEARCH. In addition to searching for an exact match, we demand that for a data structure to be called searchable, Search also has to be able to search for the closest successor or predecessor of a data item. Such a property has a tremendous advantage over just exact match, because it would allow to implement many data base applications.We are interested in finding a searchable concurrent data structure that has (1) a low degree, (2) requires a small amount of work for INSERT and DELETE operations, and (3) is able to handle concurrent search requests with low congestion and dilation.We present the first deterministic concurrent data structure, called Hyperring, that can fulfill all of these objectives in a polylogarithmic way. In fact, the Hyperring has a degree of O(log n), requires O(log3n) work for INSERT and DELETE operations, and can handle concurrent search requests to random destinations, one request per node, with congestion and dilation O(log n) w.h.p.Most of the previous solutions for distributed environments are not searchable (in our sense) but only provide exact lookup, and those that are searchable do not have proofs about the congestion caused by concurrent search requests.

Distributed systems often replicate data at multiple locations to achieve availability despite network partitions. These systems accept updates at any replica and propagate them asynchronously to every other replica. Conflict-Free Replicated Data Types (CRDTs) provide a principled approach to the problem of ensuring that replicas are eventually consistent despite the asynchronous delivery of updates.