142,161 research outputs found
A Flexible Modeling Approach for Robust Multi-Lane Road Estimation
A robust estimation of road course and traffic lanes is an essential part of
environment perception for next generations of Advanced Driver Assistance
Systems and development of self-driving vehicles. In this paper, a flexible
method for modeling multiple lanes in a vehicle in real time is presented.
Information about traffic lanes, derived by cameras and other environmental
sensors, that is represented as features, serves as input for an iterative
expectation-maximization method to estimate a lane model. The generic and
modular concept of the approach allows to freely choose the mathematical
functions for the geometrical description of lanes. In addition to the current
measurement data, the previously estimated result as well as additional
constraints to reflect parallelism and continuity of traffic lanes, are
considered in the optimization process. As evaluation of the lane estimation
method, its performance is showcased using cubic splines for the geometric
representation of lanes in simulated scenarios and measurements recorded using
a development vehicle. In a comparison to ground truth data, robustness and
precision of the lanes estimated up to a distance of 120 m are demonstrated. As
a part of the environmental modeling, the presented method can be utilized for
longitudinal and lateral control of autonomous vehicles
When parallel speedups hit the memory wall
After Amdahl's trailblazing work, many other authors proposed analytical
speedup models but none have considered the limiting effect of the memory wall.
These models exploited aspects such as problem-size variation, memory size,
communication overhead, and synchronization overhead, but data-access delays
are assumed to be constant. Nevertheless, such delays can vary, for example,
according to the number of cores used and the ratio between processor and
memory frequencies. Given the large number of possible configurations of
operating frequency and number of cores that current architectures can offer,
suitable speedup models to describe such variations among these configurations
are quite desirable for off-line or on-line scheduling decisions. This work
proposes new parallel speedup models that account for variations of the average
data-access delay to describe the limiting effect of the memory wall on
parallel speedups. Analytical results indicate that the proposed modeling can
capture the desired behavior while experimental hardware results validate the
former. Additionally, we show that when accounting for parameters that reflect
the intrinsic characteristics of the applications, such as degree of
parallelism and susceptibility to the memory wall, our proposal has significant
advantages over machine-learning-based modeling. Moreover, besides being
black-box modeling, our experiments show that conventional machine-learning
modeling needs about one order of magnitude more measurements to reach the same
level of accuracy achieved in our modeling.Comment: 24 page
Iso-energy-efficiency: An approach to power-constrained parallel computation
Future large scale high performance supercomputer systems require high energy efficiency to achieve exaflops computational power and beyond. Despite the need to understand energy efficiency in high-performance systems, there are few techniques to evaluate energy efficiency at scale. In this paper, we propose a system-level iso-energy-efficiency model to analyze, evaluate and predict energy-performance of data intensive parallel applications with various execution patterns running on large scale power-aware clusters. Our analytical model can help users explore the effects of machine and application dependent characteristics on system energy efficiency and isolate efficient ways to scale system parameters (e.g. processor count, CPU power/frequency, workload size and network bandwidth) to balance energy use and performance. We derive our iso-energy-efficiency model and apply it to the NAS Parallel Benchmarks on two power-aware clusters. Our results indicate that the model accurately predicts total system energy consumption within 5% error on average for parallel applications with various execution and communication patterns. We demonstrate effective use of the model for various application contexts and in scalability decision-making
A Supervisor for Control of Mode-switch Process
Many processes operate only around a limited number of operation points. In order to have adequate control around each operation point, and adaptive controller could be used. When the operation point changes often, a large number of parameters would have to be adapted over and over again. This makes application of conventional adaptive control unattractive, which is more suited for processes with slowly changing parameters. Furthermore, continuous adaptation is not always needed or desired. An extension of adaptive control is presented, in which for each operation point the process behaviour can be stored in a memory, retrieved from it and evaluated. These functions are co-ordinated by a ¿supervisor¿. This concept is referred to as a supervisor for control of mode-switch processes. It leads to an adaptive control structure which quickly adjusts the controller parameters based on retrieval of old information, without the need to fully relearn each time. This approach has been tested on experimental set-ups of a flexible beam and of a flexible two-link robot arm, but it is directly applicable to other processes, for instance, in the (petro) chemical industry
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An Overview of Models for Response Times and Processes in Cognitive Tests.
Response times (RTs) are a natural kind of data to investigate cognitive processes underlying cognitive test performance. We give an overview of modeling approaches and of findings obtained with these approaches. Four types of models are discussed: response time models (RT as the sole dependent variable), joint models (RT together with other variables as dependent variable), local dependency models (with remaining dependencies between RT and accuracy), and response time as covariate models (RT as independent variable). The evidence from these approaches is often not very informative about the specific kind of processes (other than problem solving, information accumulation, and rapid guessing), but the findings do suggest dual processing: automated processing (e.g., knowledge retrieval) vs. controlled processing (e.g., sequential reasoning steps), and alternative explanations for the same results exist. While it seems well-possible to differentiate rapid guessing from normal problem solving (which can be based on automated or controlled processing), further decompositions of response times are rarely made, although possible based on some of model approaches
Computing server power modeling in a data center: survey,taxonomy and performance evaluation
Data centers are large scale, energy-hungry infrastructure serving the
increasing computational demands as the world is becoming more connected in
smart cities. The emergence of advanced technologies such as cloud-based
services, internet of things (IoT) and big data analytics has augmented the
growth of global data centers, leading to high energy consumption. This upsurge
in energy consumption of the data centers not only incurs the issue of surging
high cost (operational and maintenance) but also has an adverse effect on the
environment. Dynamic power management in a data center environment requires the
cognizance of the correlation between the system and hardware level performance
counters and the power consumption. Power consumption modeling exhibits this
correlation and is crucial in designing energy-efficient optimization
strategies based on resource utilization. Several works in power modeling are
proposed and used in the literature. However, these power models have been
evaluated using different benchmarking applications, power measurement
techniques and error calculation formula on different machines. In this work,
we present a taxonomy and evaluation of 24 software-based power models using a
unified environment, benchmarking applications, power measurement technique and
error formula, with the aim of achieving an objective comparison. We use
different servers architectures to assess the impact of heterogeneity on the
models' comparison. The performance analysis of these models is elaborated in
the paper
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