Non-blocking algorithm | EPFL Graph Search

In computer science, an algorithm is called non-blocking if failure or suspension of any thread cannot cause failure or suspension of another thread; for some operations, these algorithms provide a useful alternative to traditional blocking implementations. A non-blocking algorithm is lock-free if there is guaranteed system-wide progress, and wait-free if there is also guaranteed per-thread progress. "Non-blocking" was used as a synonym for "lock-free" in the literature until the introduction of obstruction-freedom in 2003. The word "non-blocking" was traditionally used to describe telecommunications networks that could route a connection through a set of relays "without having to re-arrange existing calls" (see Clos network). Also, if the telephone exchange "is not defective, it can always make the connection" (see nonblocking minimal spanning switch). Motivation Disadvantages of locks The traditional approach to multi-threaded programming is to use locks to synchroni

Scalable Synchronization in Shared-Memory Systems: Extrapolating, Adapting, Tuning

Georgios Chatzopoulos

As hardware evolves, so do the needs of applications. To increase the performance of an application, there exist two well-known approaches. These are scaling up an application, using a larger multi-core platform, or scaling out, by distributing work to multiple machines. Both approaches are typically implemented using the shared-memory and message-passing programming models. In both programming models, and for a wide range of workloads, there is contention for access to data, and applications need synchronization. On modern multi-core platforms with tens of cores and network interconnects with sub-millisecond latencies, synchronization bottlenecks can significantly limit the scalability of an application. This dissertation studies the scalability of synchronization in shared-memory settings and focuses on the changes brought upon by modern hardware advances in processors and network interconnects. As we show, current approaches to understanding the scalability of applications, as well as prevalent synchronization primitives, are not designed for modern workloads and hardware platforms. We then propose methods and techniques that take advantage of hardware and workload knowledge to better serve application needs. We first look into better understanding the scalability of applications, using low-level performance metrics in both hardware and software. We introduce ESTIMA, an easy-to-use tool for extrapolating the scalability of in-memory applications. The key idea underlying ESTIMA is the use of stalled cycles in both hardware (e.g., cycles spent waiting for cache line fetches or busy locks), as well as software (e.g., cycles spent on synchronization, or on failed Software Transactional Memory transactions). ESTIMA measures stalled cycles on a few cores and extrapolates them to more cores, estimating the amount of waiting in the system. We then focus on locking, a common way of synchronizing data accesses in a shared-memory setting. Typically, contention for data limits the scalability of an application. A poor choice of locking algorithm can further impact scalability. We introduce GLS, a middleware that makes lock-based programming simple and effective. In contrast to classic lock libraries, GLS does not require any effort from the programmer for allocating and initializing locks, nor for selecting the appropriate locking strategy. GLS relies on GLK, a generic lock algorithm that dynamically adapts to the contention level on the lock object, delivering the best performance among simple spinlocks, scalable queue-based locks, and blocking locks. Finally, we study the performance of distributed systems that offer transactions, by exposing shared-memory semantics. Recently, such systems have seen renewed interest due to advancements in networking hardware for datacenters. We show how such systems require significant manual tuning to achieve good performance in a popular category of workloads (OLTP), and argue that doing so is error-prone, as well as time-consuming, and it needs to be repeated when the workload or the hardware change. We then introduce SPADE, a physical design tuner for OLTP workloads on modern RDMA clusters. SPADE automatically decides on data partitioning, index and storage parameters, and the right mix of direct remote data accesses and function shipping to maximize performance. To achieve this, SPADE uses low-level hardware and network performance characteristics gathered through micro-benchmarks.

EPFL2018

Safety, Liveness and Parallelism in Concurrent Computing

Victor Bushkov

The correctness of a shared object, which can be accessed by several processes concurrently, is specified through two different kinds of properties - safety and liveness. When implementing a shared object it is important to specify its correctness in a such way that it should be feasible under given assumptions. In this work we study the question of implementability of shared objects that satisfy certain safety and liveness properties under certain assumptions. In particular, we study implementability of liveness properties of transactional memory (TM) shared objects with strict serializability, a strong safety property. TM allows a programmer to encapsulate pieces of sequential code within transactions that run concurrently and it ensures that transactions run in some consistent manner by committing or aborting them. First, we give a formal definition of a liveness property in the TM context. Then, we proceed to show that certain classes of liveness properties of TM can not be implemented with strict serializability in a system in which processes are prone to failures. We also show that, in general, we cannot find a weakest non-implementable (resp. strongest implementable) liveness of TM when we require some common safety property like strict serializability. Moreover, we generalize this result to other shared object types and give a characterization of safety properties for which we can find weakest non-implementable (resp. strongest implementable) liveness. In addition to correctness properties we study the limitations of parallelism which affects performance and scalability of implementations. Specifically, we show that we cannot implement a TM which guarantees disjoint-access parallelism, which say that transactions do not need to synchronize unless they access the same application objects, while guaranteeing very weak safety and the weakest non-blocking liveness.

EPFL2015

Real Time Emotional Control for Anti-Swing and Positioning Control of SIMO Overhead Traveling Crane

Arash Arami, Babak Hosseini

Research in artificial intelligence and bioinspired algorithms is still being actively pursued in different fields of engineering. In this work, Brain Emotional Learning Based Intelligent Controller (BELBIC) is applied for real time positioning of laboratorial overhead traveling crane. This controller is based on biologically motivated algorithm originating from emotional processes in the limbic system of the mammalian brain. Simulations show that learning capability, adaptation, robustness and other control concerns of this controller are comparable with conventional techniques and lead to better performance in many cases. Two objectives, tracking desired position and keeping pendulum vertically, must be considered simultaneously. A bottom up strategy was utilized for designing the controllers. First separated BELBICs were designed for each control task. Next, in order to compensate the actual coupling between control tasks, the objective of each control tasks was considered in the stress signal of the other one. Obtained results in real tracking applications are also comparable with other conventional and intelligent approaches such as hierarchical fuzzy control (HFLC) and confirm the simulation results. Learning capability, model free control algorithm, robustness and fast response are main characteristics of this controller and designer can define emotional stress signal based on control application objectives.

2008

Scalable Synchronization in Shared-Memory Systems: Extrapolating, Adapting, Tuning

Georgios Chatzopoulos

EPFL2018