1,751 research outputs found
Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation
A human computation system can be viewed as a distributed system in which the
processors are humans, called workers. Such systems harness the cognitive power
of a group of workers connected to the Internet to execute relatively simple
tasks, whose solutions, once grouped, solve a problem that systems equipped
with only machines could not solve satisfactorily. Examples of such systems are
Amazon Mechanical Turk and the Zooniverse platform. A human computation
application comprises a group of tasks, each of them can be performed by one
worker. Tasks might have dependencies among each other. In this study, we
propose a theoretical framework to analyze such type of application from a
distributed systems point of view. Our framework is established on three
dimensions that represent different perspectives in which human computation
applications can be approached: quality-of-service requirements, design and
management strategies, and human aspects. By using this framework, we review
human computation in the perspective of programmers seeking to improve the
design of human computation applications and managers seeking to increase the
effectiveness of human computation infrastructures in running such
applications. In doing so, besides integrating and organizing what has been
done in this direction, we also put into perspective the fact that the human
aspects of the workers in such systems introduce new challenges in terms of,
for example, task assignment, dependency management, and fault prevention and
tolerance. We discuss how they are related to distributed systems and other
areas of knowledge.Comment: 3 figures, 1 tabl
Accelerating Sensitivity Analysis in Microscopy Image Segmentation Workflows
With the increasingly availability of digital microscopy imagery equipments
there is a demand for efficient execution of whole slide tissue image
applications. Through the process of sensitivity analysis it is possible to
improve the output quality of such applications, and thus, improve the desired
analysis quality. Due to the high computational cost of such analyses and the
recurrent nature of executed tasks from sensitivity analysis methods (i.e.,
reexecution of tasks), the opportunity for computation reuse arises. By
performing computation reuse we can optimize the run time of sensitivity
analysis applications. This work focuses then on finding new ways to take
advantage of computation reuse opportunities on multiple task abstraction
levels. This is done by presenting the coarse-grain merging strategy and the
new fine-grain merging algorithms, implemented on top of the Region Templates
Framework.Comment: 44 page
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
EG-ICE 2021 Workshop on Intelligent Computing in Engineering
The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways
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End-to-end deep reinforcement learning in computer systems
Abstract
The growing complexity of data processing systems has long led systems designers to imagine systems (e.g. databases, schedulers) which can self-configure and adapt based on environmental cues. In this context, reinforcement learning (RL) methods have since their inception appealed to systems developers. They promise to acquire complex decision policies from raw feedback signals. Despite their conceptual popularity, RL methods are scarcely found in real-world data processing systems. Recently, RL has seen explosive growth in interest due to high profile successes when utilising large neural networks (deep reinforcement learning). Newly emerging machine learning frameworks and powerful hardware accelerators have given rise to a plethora of new potential applications.
In this dissertation, I first argue that in order to design and execute deep RL algorithms efficiently, novel software abstractions are required which can accommodate the distinct computational patterns of communication-intensive and fast-evolving algorithms. I propose an architecture which decouples logical algorithm construction from local and distributed execution semantics. I further present RLgraph, my proof-of-concept implementation of this architecture. In RLgraph, algorithm developers can explore novel designs by constructing a high-level data flow graph through combination of logical components. This dataflow graph is independent of specific backend frameworks or notions of execution, and is only later mapped to execution semantics via a staged build process. RLgraph enables high-performing algorithm implementations while maintaining flexibility for rapid prototyping.
Second, I investigate reasons for the scarcity of RL applications in systems themselves. I argue that progress in applied RL is hindered by a lack of tools for task model design which bridge the gap between systems and algorithms, and also by missing shared standards for evaluation of model capabilities. I introduce Wield, a first-of-its-kind tool for incremental model design in applied RL. Wield provides a small set of primitives which decouple systems interfaces and deployment-specific configuration from representation. Core to Wield is a novel instructive experiment protocol called progressive randomisation which helps practitioners to incrementally evaluate different dimensions of non-determinism. I demonstrate how Wield and progressive randomisation can be used to reproduce and assess prior work, and to guide implementation of novel RL applications
Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy
Federated learning (FL) is increasingly deployed among multiple clients to
train a shared model over decentralized data. To address privacy concerns, FL
systems need to safeguard the clients' data from disclosure during training and
control data leakage through trained models when exposed to untrusted domains.
Distributed differential privacy (DP) offers an appealing solution in this
regard as it achieves a balanced tradeoff between privacy and utility without a
trusted server. However, existing distributed DP mechanisms are impractical in
the presence of client dropout, resulting in poor privacy guarantees or
degraded training accuracy. In addition, these mechanisms suffer from severe
efficiency issues.
We present Dordis, a distributed differentially private FL framework that is
highly efficient and resilient to client dropout. Specifically, we develop a
novel `add-then-remove' scheme that enforces a required noise level precisely
in each training round, even if some sampled clients drop out. This ensures
that the privacy budget is utilized prudently, despite unpredictable client
dynamics. To boost performance, Dordis operates as a distributed parallel
architecture via encapsulating the communication and computation operations
into stages. It automatically divides the global model aggregation into several
chunk-aggregation tasks and pipelines them for optimal speedup. Large-scale
deployment evaluations demonstrate that Dordis efficiently handles client
dropout in various realistic FL scenarios, achieving the optimal
privacy-utility tradeoff and accelerating training by up to 2.4
compared to existing solutions.Comment: This article has been accepted to ACM EuroSys '2
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