9,486 research outputs found
4CeeD: Real-Time Data Acquisition and Analysis Framework for Material-related Cyber-Physical Environments
In this paper, we propose a data acquisition and analysis framework for materials-to-devices processes, named 4CeeD, that focuses on the immense potential of capturing, accurately curating, correlating, and coordinating materials-to-devices digital data in a real-time and trusted manner before fully archiving and publishing them for wide access and sharing. In particular, 4CeeD consists of: (i) a curation service for collecting data from experimental instruments, curating, and wrapping of data with extensive metadata in real-time and in a trusted manner, (ii) a cloudlet for caching collected data from curation service and coordinating data transfer with the back-end, and (iii) a cloud-based coordination service for storing data, extracting meta-data, analyzing and finding correlations among the data. Our evaluation results show that our proposed approach is able to help researchers significantly save time and cost spent on experiments, and is efficient in dealing with high-volume and fast-changing workload of heterogeneous types of experimental data.National Science Foundation/NSF ACI 1443013Ope
On the capacity of information processing systems
We propose and analyze a family of information processing systems, where a
finite set of experts or servers are employed to extract information about a
stream of incoming jobs. Each job is associated with a hidden label drawn from
some prior distribution. An inspection by an expert produces a noisy outcome
that depends both on the job's hidden label and the type of the expert, and
occupies the expert for a finite time duration. A decision maker's task is to
dynamically assign inspections so that the resulting outcomes can be used to
accurately recover the labels of all jobs, while keeping the system stable.
Among our chief motivations are applications in crowd-sourcing, diagnostics,
and experiment designs, where one wishes to efficiently learn the nature of a
large number of items, using a finite pool of computational resources or human
agents.
We focus on the capacity of such an information processing system. Given a
level of accuracy guarantee, we ask how many experts are needed in order to
stabilize the system, and through what inspection architecture. Our main result
provides an adaptive inspection policy that is asymptotically optimal in the
following sense: the ratio between the required number of experts under our
policy and the theoretical optimal converges to one, as the probability of
error in label recovery tends to zero
Investigation into scalable energy and performance models for many-core systems
PhD ThesisIt is likely that many-core processor systems will continue to penetrate
emerging embedded and high-performance applications. Scalable energy and
performance models are two critical aspects that provide insights into the
conflicting trade-offs between them with growing hardware and software
complexity. Traditional performance models, such as Amdahl’s Law,
Gustafson’s and Sun-Ni’s, have helped the research community and industry
to better understand the system performance bounds with given processing
resources, which is otherwise known as speedup. However, these models and
their existing extensions have limited applicability for energy and/or
performance-driven system optimization in practical systems. For instance,
these are typically based on software characteristics, assuming ideal and
homogeneous hardware platforms or limited forms of processor
heterogeneity. In addition, the measurement of speedup and parallelization
factors of an application running on a specific hardware platform require
instrumenting the original software codes. Indeed, practical speedup and
parallelizability models of application workloads running on modern
heterogeneous hardware are critical for energy and performance models, as
they can be used to inform design and control decisions with an aim to
improve system throughput and energy efficiency.
This thesis addresses the limitations by firstly developing novel and
scalable speedup and energy consumption models based on a more general
representation of heterogeneity, referred to as the normal form heterogeneity.
A method is developed whereby standard performance counters found in
modern many-core platforms can be used to derive speedup, and therefore
the parallelizability of the software, without instrumenting applications. This
extends the usability of the new models to scenarios where the
parallelizability of software is unknown, leading to potentially Run-Time
Management (RTM) speedup and/or energy efficiency optimization. The
models and optimization methods presented in this thesis are validated
through extensive experimentation, by running a number of different
applications in wide-ranging concurrency scenarios on a number of different
homogeneous and heterogeneous Multi/Many Core Processor (M/MCP)
systems. These include homogeneous and heterogeneous architectures and
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range from existing off-the-shelf platforms to potential future system
extensions. The practical use of these models and methods is demonstrated
through real examples such as studying the effectiveness of the system load
balancer.
The models and methodologies proposed in this thesis provide guidance to
a new opportunities for improving the energy efficiency of M/MCP systemsHigher Committee of Education Development
(HCED) in Ira
Impliance: A Next Generation Information Management Appliance
ably successful in building a large market and adapting to the changes of the
last three decades, its impact on the broader market of information management
is surprisingly limited. If we were to design an information management system
from scratch, based upon today's requirements and hardware capabilities, would
it look anything like today's database systems?" In this paper, we introduce
Impliance, a next-generation information management system consisting of
hardware and software components integrated to form an easy-to-administer
appliance that can store, retrieve, and analyze all types of structured,
semi-structured, and unstructured information. We first summarize the trends
that will shape information management for the foreseeable future. Those trends
imply three major requirements for Impliance: (1) to be able to store, manage,
and uniformly query all data, not just structured records; (2) to be able to
scale out as the volume of this data grows; and (3) to be simple and robust in
operation. We then describe four key ideas that are uniquely combined in
Impliance to address these requirements, namely the ideas of: (a) integrating
software and off-the-shelf hardware into a generic information appliance; (b)
automatically discovering, organizing, and managing all data - unstructured as
well as structured - in a uniform way; (c) achieving scale-out by exploiting
simple, massive parallel processing, and (d) virtualizing compute and storage
resources to unify, simplify, and streamline the management of Impliance.
Impliance is an ambitious, long-term effort to define simpler, more robust, and
more scalable information systems for tomorrow's enterprises.Comment: This article is published under a Creative Commons License Agreement
(http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute,
display, and perform the work, make derivative works and make commercial use
of the work, but, you must attribute the work to the author and CIDR 2007.
3rd Biennial Conference on Innovative Data Systems Research (CIDR) January
710, 2007, Asilomar, California, US
InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services
Cloud computing providers have setup several data centers at different
geographical locations over the Internet in order to optimally serve needs of
their customers around the world. However, existing systems do not support
mechanisms and policies for dynamically coordinating load distribution among
different Cloud-based data centers in order to determine optimal location for
hosting application services to achieve reasonable QoS levels. Further, the
Cloud computing providers are unable to predict geographic distribution of
users consuming their services, hence the load coordination must happen
automatically, and distribution of services must change in response to changes
in the load. To counter this problem, we advocate creation of federated Cloud
computing environment (InterCloud) that facilitates just-in-time,
opportunistic, and scalable provisioning of application services, consistently
achieving QoS targets under variable workload, resource and network conditions.
The overall goal is to create a computing environment that supports dynamic
expansion or contraction of capabilities (VMs, services, storage, and database)
for handling sudden variations in service demands.
This paper presents vision, challenges, and architectural elements of
InterCloud for utility-oriented federation of Cloud computing environments. The
proposed InterCloud environment supports scaling of applications across
multiple vendor clouds. We have validated our approach by conducting a set of
rigorous performance evaluation study using the CloudSim toolkit. The results
demonstrate that federated Cloud computing model has immense potential as it
offers significant performance gains as regards to response time and cost
saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape
A general guide to applying machine learning to computer architecture
The resurgence of machine learning since the late 1990s has been enabled by significant advances in computing performance and the growth of big data. The ability of these algorithms to detect complex patterns in data which are extremely difficult to achieve manually, helps to produce effective predictive models. Whilst computer architects have been accelerating the performance of machine learning algorithms with GPUs and custom hardware, there have been few implementations leveraging these algorithms to improve the computer system performance. The work that has been conducted, however, has produced considerably promising results.
The purpose of this paper is to serve as a foundational base and guide to future computer
architecture research seeking to make use of machine learning models for improving system efficiency.
We describe a method that highlights when, why, and how to utilize machine learning
models for improving system performance and provide a relevant example showcasing the effectiveness of applying machine learning in computer architecture. We describe a process of data
generation every execution quantum and parameter engineering. This is followed by a survey of a
set of popular machine learning models. We discuss their strengths and weaknesses and provide
an evaluation of implementations for the purpose of creating a workload performance predictor
for different core types in an x86 processor. The predictions can then be exploited by a scheduler
for heterogeneous processors to improve the system throughput. The algorithms of focus are
stochastic gradient descent based linear regression, decision trees, random forests, artificial neural
networks, and k-nearest neighbors.This work has been supported by the European Research Council (ERC) Advanced Grant RoMoL (Grant Agreemnt 321253) and by the Spanish Ministry of Science and Innovation (contract TIN 2015-65316P).Peer ReviewedPostprint (published version
A Conceptual Architecture for a Quantum-HPC Middleware
Quantum computing promises potential for science and industry by solving
certain computationally complex problems faster than classical computers.
Quantum computing systems evolved from monolithic systems towards modular
architectures comprising multiple quantum processing units (QPUs) coupled to
classical computing nodes (HPC). With the increasing scale, middleware systems
that facilitate the efficient coupling of quantum-classical computing are
becoming critical. Through an in-depth analysis of quantum applications,
integration patterns and systems, we identified a gap in understanding
Quantum-HPC middleware systems. We present a conceptual middleware to
facilitate reasoning about quantum-classical integration and serve as the basis
for a future middleware system. An essential contribution of this paper lies in
leveraging well-established high-performance computing abstractions for
managing workloads, tasks, and resources to integrate quantum computing into
HPC systems seamlessly.Comment: 12 pages, 3 figure
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