282 research outputs found
Revisiting Actor Programming in C++
The actor model of computation has gained significant popularity over the
last decade. Its high level of abstraction makes it appealing for concurrent
applications in parallel and distributed systems. However, designing a
real-world actor framework that subsumes full scalability, strong reliability,
and high resource efficiency requires many conceptual and algorithmic additives
to the original model.
In this paper, we report on designing and building CAF, the "C++ Actor
Framework". CAF targets at providing a concurrent and distributed native
environment for scaling up to very large, high-performance applications, and
equally well down to small constrained systems. We present the key
specifications and design concepts---in particular a message-transparent
architecture, type-safe message interfaces, and pattern matching
facilities---that make native actors a viable approach for many robust,
elastic, and highly distributed developments. We demonstrate the feasibility of
CAF in three scenarios: first for elastic, upscaling environments, second for
including heterogeneous hardware like GPGPUs, and third for distributed runtime
systems. Extensive performance evaluations indicate ideal runtime behaviour for
up to 64 cores at very low memory footprint, or in the presence of GPUs. In
these tests, CAF continuously outperforms the competing actor environments
Erlang, Charm++, SalsaLite, Scala, ActorFoundry, and even the OpenMPI.Comment: 33 page
A Configurable Transport Layer for CAF
The message-driven nature of actors lays a foundation for developing scalable
and distributed software. While the actor itself has been thoroughly modeled,
the message passing layer lacks a common definition. Properties and guarantees
of message exchange often shift with implementations and contexts. This adds
complexity to the development process, limits portability, and removes
transparency from distributed actor systems.
In this work, we examine actor communication, focusing on the implementation
and runtime costs of reliable and ordered delivery. Both guarantees are often
based on TCP for remote messaging, which mixes network transport with the
semantics of messaging. However, the choice of transport may follow different
constraints and is often governed by deployment. As a first step towards
re-architecting actor-to-actor communication, we decouple the messaging
guarantees from the transport protocol. We validate our approach by redesigning
the network stack of the C++ Actor Framework (CAF) so that it allows to combine
an arbitrary transport protocol with additional functions for remote messaging.
An evaluation quantifies the cost of composability and the impact of individual
layers on the entire stack
OpenCL Actors - Adding Data Parallelism to Actor-based Programming with CAF
The actor model of computation has been designed for a seamless support of
concurrency and distribution. However, it remains unspecific about data
parallel program flows, while available processing power of modern many core
hardware such as graphics processing units (GPUs) or coprocessors increases the
relevance of data parallelism for general-purpose computation.
In this work, we introduce OpenCL-enabled actors to the C++ Actor Framework
(CAF). This offers a high level interface for accessing any OpenCL device
without leaving the actor paradigm. The new type of actor is integrated into
the runtime environment of CAF and gives rise to transparent message passing in
distributed systems on heterogeneous hardware. Following the actor logic in
CAF, OpenCL kernels can be composed while encapsulated in C++ actors, hence
operate in a multi-stage fashion on data resident at the GPU. Developers are
thus enabled to build complex data parallel programs from primitives without
leaving the actor paradigm, nor sacrificing performance. Our evaluations on
commodity GPUs, an Nvidia TESLA, and an Intel PHI reveal the expected linear
scaling behavior when offloading larger workloads. For sub-second duties, the
efficiency of offloading was found to largely differ between devices. Moreover,
our findings indicate a negligible overhead over programming with the native
OpenCL API.Comment: 28 page
Functional programming languages in computing clouds: practical and theoretical explorations
Cloud platforms must integrate three pillars: messaging, coordination of workers and data. This research investigates whether functional programming languages have any special merit when it comes to the implementation of cloud computing platforms. This thesis presents the lightweight message queue CMQ and the DSL CWMWL for the coordination of workers that we use as artefact to proof or disproof the special merit of functional programming languages in computing clouds. We have detailed the design and implementation with the broad aim to match the notions and the requirements of computing clouds. Our approach to evaluate these aims is based on evaluation criteria that are based on a series of comprehensive rationales and specifics that allow the FPL Haskell to be thoroughly analysed. We find that Haskell is excellent for use cases that do not require the distribution of the application across the boundaries of (physical or virtual) systems, but not appropriate as a whole for the development of distributed cloud based workloads that require communication with the far side and coordination of decoupled workloads. However, Haskell may be able to qualify as a suitable vehicle in the future with future developments of formal mechanisms that embrace non-determinism in the underlying distributed environments leading to applications that are anti-fragile rather than applications that insist on strict determinism that can only be guaranteed on the local system or via slow blocking communication mechanisms
Improving the Performance of User-level Runtime Systems for Concurrent Applications
Concurrency is an essential part of many modern large-scale software systems. Applications must handle millions of simultaneous requests from millions of connected devices. Handling
such a large number of concurrent requests requires runtime systems that efficiently man-
age concurrency and communication among tasks in an application across multiple cores.
Existing low-level programming techniques provide scalable solutions with low overhead,
but require non-linear control flow. Alternative approaches to concurrent programming,
such as Erlang and Go, support linear control flow by mapping multiple user-level execution
entities across multiple kernel threads (M:N threading). However, these systems provide
comprehensive execution environments that make it difficult to assess the performance
impact of user-level runtimes in isolation.
This thesis presents a nimble M:N user-level threading runtime that closes this con-
ceptual gap and provides a software infrastructure to precisely study the performance
impact of user-level threading. Multiple design alternatives are presented and evaluated
for scheduling, I/O multiplexing, and synchronization components of the runtime. The
performance of the runtime is evaluated in comparison to event-driven software, system-
level threading, and other user-level threading runtimes. An experimental evaluation is
conducted using benchmark programs, as well as the popular Memcached application.
The user-level runtime supports high levels of concurrency without sacrificing application
performance. In addition, the user-level scheduling problem is studied in the context of
an existing actor runtime that maps multiple actors to multiple kernel-level threads. In
particular, two locality-aware work-stealing schedulers are proposed and evaluated. It is
shown that locality-aware scheduling can significantly improve the performance of a class
of applications with a high level of concurrency. In general, the performance and resource
utilization of large-scale concurrent applications depends on the level of concurrency that
can be expressed by the programming model. This fundamental effect is studied by refining
and customizing existing concurrency models
React++: A Lightweight Actor Framework in C++
Distributed software remains susceptible to data races and poor scalability because of the widespread use of locks and other low-level synchronization primitives. Furthermore, using this programming approach is known to break encapsulation offered by object-oriented programming. Actors present an alternative model of concurrent computation by serving as building blocks with a higher level of abstraction. They encapsulate concurrent logic in their behaviors and rely only on asynchronous exchange of messages for synchronization, preventing a broad range of concurrent issues by eschewing locks. Existing actor frameworks often seem to focus on CPU-bound workloads and lack an actor-oriented I/O infrastructure. The purpose of this thesis is to investigate the scalability of user-space I/O operations carried out by actors. It presents an experimental actor framework named React++, with an M:N runtime for cooperative scheduling of actors and an integrated I/O subsystem. Load distribution is policy-driven and uses a variant of the randomized work-stealing algorithm. The evaluation of the framework is carried out in three stages. First, the efficiency of message delivery, scheduling and load balancing is assessed by a set of micro-benchmarks, where React++ retains a competitive score against several well-known actor frameworks. Next, a web server built on React++ is shown to be on par with its fastest event-driven counterparts in the TechEmpower plaintext benchmark. Finally, the runtime of an existing messaging library (ZeroMQ) is augmented with React++, replacing the backend and delegating all network I/O to actors without incurring any substantial overhead
The Internet of Things and The Web of Things
International audienceThe Internet of Things is creating a new world, a quantifiable and measureable world, where people and businesses can manage their assets in better informed ways, and can make more timely and better informed decisions about what they want or need to do. This new con-nected world brings with it fundamental changes to society and to consumers. This special issue of ERCIM News thus focuses on various relevant aspects of the Internet of Things and the Web of Things
RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems
The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness
- …