IST Austria Technical Report

In order to guarantee that each method of a data structure updates the logical state exactly once, al-most all non-blocking implementations employ Compare-And-Swap (CAS) based synchronization. For FIFO queue implementations this translates into concurrent enqueue or dequeue methods competing among themselves to update the same variable, the tail or the head, respectively, leading to high contention and poor scalability. Recent non-blocking queue implementations try to alleviate high contentionby increasing the number of contention points, all the while using CAS-based synchronization. Furthermore, obtaining a wait-free implementation with competition is achieved by additional synchronization which leads to further degradation of performance.In this paper we formalize the notion of competitiveness of a synchronizing statement which can beused as a measure for the scalability of concurrent implementations. We present a new queue implementation, the Speculative Pairing (SP) queue, which, as we show, decreases competitiveness by using Fetch-And-Increment (FAI) instead of CAS. We prove that the SP queue is linearizable and lock-free.We also show that replacing CAS with FAI leads to wait-freedom for dequeue methods without an adverse effect on performance. In fact, our experiments suggest that the SP queue can perform and scale better than the state-of-the-art queue implementations

Performance Evaluation of Blocking and Non-Blocking Concurrent Queues on GPUs

The efficiency of concurrent data structures is crucial to the performance of multi-threaded programs in shared-memory systems. The arbitrary execution of concurrent threads, however, can result in an incorrect behavior of these data structures. Graphics Processing Units (GPUs) have appeared as a powerful platform for high-performance computing. As regular data-parallel computations are straightforward to implement on traditional CPU architectures, it is challenging to implement them in a SIMD environment in the presence of thousands of active threads on GPU architectures. In this thesis, we implement a concurrent queue data structure and evaluate its performance on GPUs to understand how it behaves in a massively-parallel GPU environment. We implement both blocking and non-blocking approaches and compare their performance and behavior using both micro-benchmark and real-world application. We provide a complete evaluation and analysis of our implementations on an AMD Radeon R7 GPU. Our experiment shows that non-blocking approach outperforms blocking approach by up to 15.1 times when sufficient thread-level parallelism is present

Lock-free Concurrent Data Structures

Concurrent data structures are the data sharing side of parallel programming. Data structures give the means to the program to store data, but also provide operations to the program to access and manipulate these data. These operations are implemented through algorithms that have to be efficient. In the sequential setting, data structures are crucially important for the performance of the respective computation. In the parallel programming setting, their importance becomes more crucial because of the increased use of data and resource sharing for utilizing parallelism. The first and main goal of this chapter is to provide a sufficient background and intuition to help the interested reader to navigate in the complex research area of lock-free data structures. The second goal is to offer the programmer familiarity to the subject that will allow her to use truly concurrent methods.Comment: To appear in "Programming Multi-core and Many-core Computing Systems", eds. S. Pllana and F. Xhafa, Wiley Series on Parallel and Distributed Computin

Concurrent Data Structures Linked in Time

Arguments about correctness of a concurrent data structure are typically carried out by using the notion of linearizability and specifying the linearization points of the data structure's procedures. Such arguments are often cumbersome as the linearization points' position in time can be dynamic (depend on the interference, run-time values and events from the past, or even future), non-local (appear in procedures other than the one considered), and whose position in the execution trace may only be determined after the considered procedure has already terminated. In this paper we propose a new method, based on a separation-style logic, for reasoning about concurrent objects with such linearization points. We embrace the dynamic nature of linearization points, and encode it as part of the data structure's auxiliary state, so that it can be dynamically modified in place by auxiliary code, as needed when some appropriate run-time event occurs. We name the idea linking-in-time, because it reduces temporal reasoning to spatial reasoning. For example, modifying a temporal position of a linearization point can be modeled similarly to a pointer update in separation logic. Furthermore, the auxiliary state provides a convenient way to concisely express the properties essential for reasoning about clients of such concurrent objects. We illustrate the method by verifying (mechanically in Coq) an intricate optimal snapshot algorithm due to Jayanti, as well as some clients

An Efficient Data Exchange Algorithm for Chained Network Functions

In-network function chaining often involves the deployment of multiple applications into a single, possibly multi-tenant, middlebox. This approach has gained much interest since new network paradigms, such as Software Defined Networking (SDN) and Network Function Virtualization (NFV), have been proposed to virtualize resources as well as network functions. In this scenario, it is very common to move data (e.g., packets) from an application to another by means of a switching module that is in charge of chaining network functions in the correct order, also ensuring an adequate level of isolation between any two virtualized components. With this purpose in mind, this paper proposes an efficient algorithm to handle the communication between the internal soft-switch and the heterogeneous network functions that are executed on the same server. Our proposal is designed with the aim of dealing with high speed packet processing, hence an extensive performance evaluation is also provided to prove the goodness of our solution in this context

IST Austria Technical Report

Linearizability requires that the outcome of calls by competing threads to a concurrent data structure is the same as some sequential execution where each thread has exclusive access to the data structure. In an ordered data structure, such as a queue or a stack, linearizability is ensured by requiring threads commit in the order dictated by the sequential semantics of the data structure; e.g., in a concurrent queue implementation a dequeue can only remove the oldest element. In this paper, we investigate the impact of this strict ordering, by comparing what linearizability allows to what existing implementations do. We first give an operational definition for linearizability which allows us to build the most general linearizable implementation as a transition system for any given sequential specification. We then use this operational definition to categorize linearizable implementations based on whether they are bound or free. In a bound implementation, whenever all threads observe the same logical state, the updates to the logical state and the temporal order of commits coincide. All existing queue implementations we know of are bound. We then proceed to present, to the best of our knowledge, the first ever free queue implementation. Our experiments show that free implementations have the potential for better performance by suffering less from contention

An efficient data exchange mechanism for chained network functions

Thanks to the increasing success of virtualization technologies and processing capabilities of computing devices, the deployment of virtual network functions is evolving towards a unified approach aiming at concentrating a huge amount of such functions within a limited number of commodity servers. To keep pace with this trend, a key issue to address is the definition of a secure and efficient way to move data between the different virtualized environments hosting the functions and a centralized component that builds the function chains within a single server. This paper proposes an efficient algorithm that realizes this vision and that, by exploiting the peculiarities of this application domain, is more efficient than classical solutions. The algorithm that manages the data exchanges is validated by performing a formal verification of its main safety and security properties, and an extensive functional and performance evaluation is presented