Shared resource

Summary

In computing, a shared resource, or network share, is a computer resource made available from one host to other hosts on a computer network. It is a device or piece of information on a computer that can be remotely accessed from another computer transparently as if it were a resource in the local machine. Network sharing is made possible by inter-process communication over the network. Some examples of shareable resources are computer programs, data, storage devices, and printers. E.g. shared file access (also known as disk sharing and folder sharing), shared printer access, shared scanner access, etc. The shared resource is called a shared disk, shared folder or shared document The term traditionally means shared file access, especially in the context of operating systems and LAN and Intranet services, for example in Microsoft Windows documentation. Though, as BitTorrent and similar applications became available in the early 2000s, the term file sharing

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Resource Sharing in Dataflow Circuits

Andrea Guerrieri, Paolo Ienne, Lana Josipovic, Axel Marmet

To achieve resource-efficient hardware designs, HLS tools share (i.e., time-multiplex) functional units among operations of the same type. This optimization is typically performed together with operation scheduling to ensure the best possible unit usage at each point in time. Dataflow circuits have emerged as an alternative HLS approach to efficiently handle irregular and control-dominated code. Yet, these circuits do not have a predetermined schedule-in its absence, it is challenging to determine which operations can share a functional unit without a performance penalty. Furthermore, although sharing seems to imply only some trivial circuitry, time-multiplexing units in dataflow circuits may cause deadlock by blocking certain data transfers and preventing operations from executing. In this paper, we present a technique to automatically identify performance-acceptable resource sharing opportunities in dataflow circuits and we describe a sharing mechanism that achieves deadlock-free dataflow designs. On benchmarks obtained from C code, we show that our approach effectively implements resource sharing: it results in significant area savings (i.e., a DSP reduction of up to 81%) compared to dataflow circuits which do not support this feature and matches the sharing capabilities of a state-of-the-art HLS tool.

IEEE2022

g-Social: Enhancing Integrated e-Science Tools with Social Networking Functionality

Andriani Stylianou

During the last decade, the scientific community has witnessed an unprecedented deployment of large-scale, federated e-Infrastructures such as Grid Computing, primarily for supporting data-intensive scientific exploration and coordinated problem solving. However, practical experience and user studies have indicated that the adoption of such e-Infrastructures is lagging behind original expectations, a fact which is mainly attributed to the limited support that available tools provide for user collaboration and information sharing. The goal of this paper is twofold, first to lay down the foundations for building a collaboration environment in the form of abstractions and second to show the effectiveness of these abstractions through g-Social, an Eclipse-based, open-source environment as an extension to g-Eclipse, that provides a powerful, user-friendly, platform-independent toolset for users, application developers and administrators of Grid infrastructures. g-Social enables user collaboration and resource sharing through Online Social Networking services, capitalizing on the success that these services have.

Ieee2012

A plethora of real world problems consist of a number of agents that interact, learn, cooperate, coordinate, and compete with others in ever more complex environments. Examples include autonomous vehicles, robotic agents, intelligent infrastructure, IoT devices, and so on. As more and more autonomous agents are deployed in the real-world, it will bring forth the need for novel algorithms, theory, and tools to enable coordination on a massive scale. In this thesis, we develop such tools to tackle two central challenges in multi-agent coordination research: solving allocation problems, and resource sharing, focusing on solutions that are scalable, practical, and applicable to real-world problems.In the first part of the thesis we tackle the problem of allocating resources to agents, i.e., solving a weighted matching problem. Real-world matching problems may occur in massively large systems, they are distributed and information-restrictive, and individuals have to reveal their preferences over the possible matches in order to get a high quality match, which brings forth significant privacy risks. As such, there are three main challenges: complexity, communication, and privacy.Our proposed approach, ALMA, is a practical heuristic designed for real-world, large-scale (

10^6

agents) applications. It is based on a simple altruistic behavioral convention: agents have a higher probability to back-off from contesting a resource if they have good alternatives, potentially freeing the resource for some agent that does not. ALMA tackles all of the aforementioned challenges: it is decentralized, runs on-device, requires no inter-agent communication, converges in constant time -- under reasonable assumptions --, and provides strong, worst-case, privacy guarantees. Moreover, by incorporating learning we can mitigate the loss in social welfare and increase fairness. Finally, rational agents can use such simple conventions, along with an arbitrary signal from the environment, to learn a correlated equilibrium for accessing a set resources, under high congestion.In the second part of the thesis we focus on a critical open problem: the question of cooperation in socio-ecological and socio-economical systems, and sustainability in the use of common-pool resources. In recent years, learning agents, especially deep reinforcement learning agents, have become ubiquitous in such systems. Yet, scaling to environments with a large number of agents and low observability continues to be a challenge. In our work, we focus on common-pool resources. Individuals face strong incentives to appropriate, which results in overuse and even the depletion of the resources. Our goal is to apply simple interventions to steer the population to desirable states.We propose a simple, yet powerful, and robust technique: allow agents to observe an arbitrary common signal from the environment. The agents learn to couple their policies, and avoid depletion in a wider range of settings, while achieving higher social welfare and convergence speed.Finally, we propose a practical approach to computing market prices and allocations via a deep reinforcement learning policymaker agent. Compared to the idealized market equilibrium outcome -- which can not always be efficiently computed -- our policymaker is much more flexible, allowing us to tune the prices with regard to diverse objectives such as sustainability and resource wastefulness, fairness, buyers' and sellers' welfare, etc.

EPFL2022

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EPFL2022