21,778 research outputs found
Cost projections for Redox Energy storage systems
A preliminary design and system cost analysis was performed for the redox energy storage system. A conceptual design and cost estimate was prepared for each of two energy applications: (1) electric utility 100-MWh requirement (10-MW for ten hours) for energy storage for utility load leveling application, and (2) a 500-kWh requirement (10-kW for 50 hours) for use with a variety of residential or commercial applications, including stand alone solar photovoltaic systems. The conceptual designs were based on cell performance levels, system design parameters, and special material costs. These data were combined with estimated thermodynamic and hydraulic analysis to provide preliminary system designs. Results indicate that the redox cell stack to be amenable to mass production techniques with a relatively low material cost
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
Performance Characterization of In-Memory Data Analytics on a Modern Cloud Server
In last decade, data analytics have rapidly progressed from traditional
disk-based processing to modern in-memory processing. However, little effort
has been devoted at enhancing performance at micro-architecture level. This
paper characterizes the performance of in-memory data analytics using Apache
Spark framework. We use a single node NUMA machine and identify the bottlenecks
hampering the scalability of workloads. We also quantify the inefficiencies at
micro-architecture level for various data analysis workloads. Through empirical
evaluation, we show that spark workloads do not scale linearly beyond twelve
threads, due to work time inflation and thread level load imbalance. Further,
at the micro-architecture level, we observe memory bound latency to be the
major cause of work time inflation.Comment: Accepted to The 5th IEEE International Conference on Big Data and
Cloud Computing (BDCloud 2015
An ultrahigh-speed digitizer for the Harvard College Observatory astronomical plates
A machine capable of digitizing two 8 inch by 10 inch (203 mm by 254 mm)
glass astrophotographic plates or a single 14 inch by 17 inch (356 mm by 432
mm) plate at a resolution of 11 microns per pixel or 2309 dots per inch (dpi)
in 92 seconds is described. The purpose of the machine is to digitize the
\~500,000 plate collection of the Harvard College Observatory in a five year
time frame. The digitization must meet the requirements for scientific work in
astrometry, photometry, and archival preservation of the plates. This paper
describes the requirements for and the design of the subsystems of the machine
that was developed specifically for this task.Comment: 12 pages, 9 figures, 1 table; presented at SPIE (July, 2006) and
published in Proceeding
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