41,914 research outputs found
Towards co-designed optimizations in parallel frameworks: A MapReduce case study
The explosion of Big Data was followed by the proliferation of numerous
complex parallel software stacks whose aim is to tackle the challenges of data
deluge. A drawback of a such multi-layered hierarchical deployment is the
inability to maintain and delegate vital semantic information between layers in
the stack. Software abstractions increase the semantic distance between an
application and its generated code. However, parallel software frameworks
contain inherent semantic information that general purpose compilers are not
designed to exploit.
This paper presents a case study demonstrating how the specific semantic
information of the MapReduce paradigm can be exploited on multicore
architectures. MR4J has been implemented in Java and evaluated against
hand-optimized C and C++ equivalents. The initial observed results led to the
design of a semantically aware optimizer that runs automatically without
requiring modification to application code.
The optimizer is able to speedup the execution time of MR4J by up to 2.0x.
The introduced optimization not only improves the performance of the generated
code, during the map phase, but also reduces the pressure on the garbage
collector. This demonstrates how semantic information can be harnessed without
sacrificing sound software engineering practices when using parallel software
frameworks.Comment: 8 page
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Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings
Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions
A Practical Blended Analysis for Dynamic Features in JavaScript
The JavaScript Blended Analysis Framework is designed to
perform a general-purpose, practical combined static/dynamic
analysis of JavaScript programs, while handling dynamic
features such as run-time generated code and variadic func-
tions. The idea of blended analysis is to focus static anal-
ysis on a dynamic calling structure collected at runtime in
a lightweight manner, and to rene the static analysis us-
ing additional dynamic information. We perform blended
points-to analysis of JavaScript with our framework and
compare results with those computed by a pure static points-
to analysis. Using JavaScript codes from actual webpages
as benchmarks, we show that optimized blended analysis
for JavaScript obtains good coverage (86.6% on average per
website) of the pure static analysis solution and nds ad-
ditional points-to pairs (7.0% on average per website) con-
tributed by dynamically generated/loaded code
Implementation of Nonlinear Model Predictive Path-Following Control for an Industrial Robot
Many robotic applications, such as milling, gluing, or high precision
measurements, require the exact following of a pre-defined geometric path. In
this paper, we investigate the real-time feasible implementation of model
predictive path-following control for an industrial robot. We consider
constrained output path following with and without reference speed assignment.
We present results from an implementation of the proposed model predictive
path-following controller on a KUKA LWR IV robot.Comment: 8 pages, 3 figures; final revised versio
Electrical Power Working Group report
The status of and need for power technologies for Spacecraft 2000 were assessed and development programs required to establish an achievable and competitive technology base for spacecraft of the 21st century were identified. The results are summarized, including the recommendations and the underlying rationale
Why (and How) Networks Should Run Themselves
The proliferation of networked devices, systems, and applications that we
depend on every day makes managing networks more important than ever. The
increasing security, availability, and performance demands of these
applications suggest that these increasingly difficult network management
problems be solved in real time, across a complex web of interacting protocols
and systems. Alas, just as the importance of network management has increased,
the network has grown so complex that it is seemingly unmanageable. In this new
era, network management requires a fundamentally new approach. Instead of
optimizations based on closed-form analysis of individual protocols, network
operators need data-driven, machine-learning-based models of end-to-end and
application performance based on high-level policy goals and a holistic view of
the underlying components. Instead of anomaly detection algorithms that operate
on offline analysis of network traces, operators need classification and
detection algorithms that can make real-time, closed-loop decisions. Networks
should learn to drive themselves. This paper explores this concept, discussing
how we might attain this ambitious goal by more closely coupling measurement
with real-time control and by relying on learning for inference and prediction
about a networked application or system, as opposed to closed-form analysis of
individual protocols
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