4,817 research outputs found
A Survey of Prediction and Classification Techniques in Multicore Processor Systems
In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems
Enabling Self-aware Smart Buildings by Augmented Reality
Conventional HVAC control systems are usually incognizant of the physical
structures and materials of buildings. These systems merely follow pre-set HVAC
control logic based on abstract building thermal response models, which are
rough approximations to true physical models, ignoring dynamic spatial
variations in built environments. To enable more accurate and responsive HVAC
control, this paper introduces the notion of "self-aware" smart buildings, such
that buildings are able to explicitly construct physical models of themselves
(e.g., incorporating building structures and materials, and thermal flow
dynamics). The question is how to enable self-aware buildings that
automatically acquire dynamic knowledge of themselves. This paper presents a
novel approach using "augmented reality". The extensive user-environment
interactions in augmented reality not only can provide intuitive user
interfaces for building systems, but also can capture the physical structures
and possibly materials of buildings accurately to enable real-time building
simulation and control. This paper presents a building system prototype
incorporating augmented reality, and discusses its applications.Comment: This paper appears in ACM International Conference on Future Energy
Systems (e-Energy), 201
Enhancing Energy Production with Exascale HPC Methods
High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose
processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale
simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of
Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and
from the Brazilian Ministry of Science, Technology and Innovation through Rede
Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the
Intel Corporation, which enabled us to obtain the presented experimental results in
uncertainty quantification in seismic imagingPostprint (author's final draft
A survey on scheduling and mapping techniques in 3D Network-on-chip
Network-on-Chips (NoCs) have been widely employed in the design of
multiprocessor system-on-chips (MPSoCs) as a scalable communication solution.
NoCs enable communications between on-chip Intellectual Property (IP) cores and
allow those cores to achieve higher performance by outsourcing their
communication tasks. Mapping and Scheduling methodologies are key elements in
assigning application tasks, allocating the tasks to the IPs, and organising
communication among them to achieve some specified objectives. The goal of this
paper is to present a detailed state-of-the-art of research in the field of
mapping and scheduling of applications on 3D NoC, classifying the works based
on several dimensions and giving some potential research directions
CoMeT: An Integrated Interval Thermal Simulation Toolchain for 2D, 2.5 D, and 3D Processor-Memory Systems
Processing cores and the accompanying main memory working in tandem enable
the modern processors. Dissipating heat produced from computation, memory
access remains a significant problem for processors. Therefore, processor
thermal management continues to be an active research topic. Most thermal
management research takes place using simulations, given the challenges of
measuring temperature in real processors. Since core and memory are fabricated
on separate packages in most existing processors, with the memory having lower
power densities, thermal management research in processors has primarily
focused on the cores.
Memory bandwidth limitations associated with 2D processors lead to
high-density 2.5D and 3D packaging technology. 2.5D packaging places cores and
memory on the same package. 3D packaging technology takes it further by
stacking layers of memory on the top of cores themselves. Such packagings
significantly increase the power density, making processors prone to heating.
Therefore, mitigating thermal issues in high-density processors (packaged with
stacked memory) becomes an even more pressing problem. However, given the lack
of thermal modeling for memories in existing interval thermal simulation
toolchains, they are unsuitable for studying thermal management for
high-density processors.
To address this issue, we present CoMeT, the first integrated Core and Memory
interval Thermal simulation toolchain. CoMeT comprehensively supports thermal
simulation of high- and low-density processors corresponding to four different
core-memory configurations - off-chip DDR memory, off-chip 3D memory, 2.5D, and
3D. CoMeT supports several novel features that facilitate overlying system
research. Compared to an equivalent state-of-the-art core-only toolchain, CoMeT
adds only a ~5% simulation-time overhead. The source code of CoMeT has been
made open for public use under the MIT license.Comment: https://github.com/marg-tools/CoMe
Modeling and optimization of high-performance many-core systems for energy-efficient and reliable computing
Thesis (Ph.D.)--Boston UniversityMany-core systems, ranging from small-scale many-core processors to large-scale high performance computing (HPC) data centers, have become the main trend in computing system design owing to their potential to deliver higher throughput per watt. However, power densities and temperatures increase following the growth in the performance capacity, and bring major challenges in energy efficiency, cooling costs, and reliability. These challenges require a joint assessment of performance, power, and temperature tradeoffs as well as the design of runtime optimization techniques that monitor and manage the interplay among them. This thesis proposes novel modeling and runtime management techniques that evaluate and optimize the performance, energy, and reliability of many-core systems.
We first address the energy and thermal challenges in 3D-stacked many-core processors. 3D processors with stacked DRAM have the potential to dramatically improve performance owing to lower memory access latency and higher bandwidth. However, the performance increase may cause 3D systems to exceed the power budgets or create thermal hot spots. In order to provide an accurate analysis and enable the design of efficient management policies, this thesis introduces a simulation framework to jointly analyze performance, power, and temperature for 3D systems. We then propose a runtime optimization policy that maximizes the system performance by characterizing the application behavior and predicting the operating points that satisfy the power and thermal constraints. Our policy reduces the energy-delay product (EDP) by up to 61.9% compared to existing strategies.
Performance, cooling energy, and reliability are also critical aspects in HPC data centers. In addition to causing reliability degradation, high temperatures increase the required cooling energy. Communication cost, on the other hand, has a significant impact on system performance in HPC data centers. This thesis proposes a topology-aware technique that maximizes system reliability by selecting between workload clustering and balancing. Our policy improves the system reliability by up to 123.3% compared to existing temperature balancing approaches. We also introduce a job allocation methodology to simultaneously optimize the communication cost and the cooling energy in a data center. Our policy reduces the cooling cost by 40% compared to cooling-aware and performance-aware policies, while achieving comparable performance to performance-aware policy
Limits on Fundamental Limits to Computation
An indispensable part of our lives, computing has also become essential to
industries and governments. Steady improvements in computer hardware have been
supported by periodic doubling of transistor densities in integrated circuits
over the last fifty years. Such Moore scaling now requires increasingly heroic
efforts, stimulating research in alternative hardware and stirring controversy.
To help evaluate emerging technologies and enrich our understanding of
integrated-circuit scaling, we review fundamental limits to computation: in
manufacturing, energy, physical space, design and verification effort, and
algorithms. To outline what is achievable in principle and in practice, we
recall how some limits were circumvented, compare loose and tight limits. We
also point out that engineering difficulties encountered by emerging
technologies may indicate yet-unknown limits.Comment: 15 pages, 4 figures, 1 tabl
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