scholarly journals Minimizing Power Consumption of an Experimental HVAC System Based on Parallel Grid Searching

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2083 ◽  
Author(s):  
Wangqi Xiong ◽  
Jiandong Wang
Keyword(s):  
Power Consumption ◽  
Graphics Processing Units ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Experimental Studies ◽  
Optimal Solution ◽  
Hvac System ◽  
Convex Optimization Problem ◽  
The One ◽  
Graphics Processing

This paper proposes a parallel grid search algorithm to find an optimal operating point for minimizing the power consumption of an experimental heating, ventilating and air conditioning (HVAC) system. First, a multidimensional, nonlinear and non-convex optimization problem subject to constraints is formulated based on a semi-physical model of the experimental HVAC system. Second, the optimization problem is parallelized based on Graphics Processing Units to simultaneously compute optimization loss functions for different solutions in a searching grid, and to find the optimal solution as the one having the minimum loss function. The proposed algorithm has an advantage that the optimal solution is known with evidence as to the best one subject to current resolutions of the searching grid. Experimental studies are provided to support the proposed algorithm.

Design of Low Order Controllers for Decoupled MIMO Systems With Time Response Specifications

2018 ◽  
pp. 90-128
Author(s):  
Maher Ben Hariz ◽  
Wassila Chagra ◽  
Faouzi Bouani
Keyword(s):  
Convex Optimization ◽  
Optimization Problem ◽  
Closed Loop ◽  
Mimo Systems ◽  
Design Method ◽  
Optimal Solution ◽  
Time Response ◽  
Convex Optimization Problem ◽  
Tito Systems ◽  
Low Order

The design of a low order controller for decoupled MIMO systems is proposed. The main objective of this controller is to guarantee some closed loop time response performances such as the settling time and the overshoot. The controller parameters are obtained by resolving a non-convex optimization problem. In order to obtain an optimal solution, the use of a global optimization method is suggested. In this chapter, the proposed solution is the GGP method. The principle of this method consists of transforming a non-convex optimization problem to a convex one by some mathematical transformations. So as to accomplish the fixed goal, it is imperative to decouple the coupled MIMO systems. To approve the controllers' design method, the synthesis of fixed low order controller for decoupled TITO systems is presented firstly. Then, this design method is generalized in the case of MIMO systems. Simulation results and a comparison study between the presented approach and a PI controller are given in order to show the efficiency of the proposed controller. It is remarkable that the obtained solution meets the desired closed loop time specifications for each system output. It is also noted that by considering the proposed approach the user can fix the desired closed loop performances for each output independently.

Data-Parallel Techniques for Agent-Based Tissue Modeling on Graphics Processing Units

2008 ◽  
Author(s):  
Ryan S. Richards ◽  
Mikola Lysenko ◽  
Roshan M. D’Souza ◽  
Gary An
Keyword(s):  
Real Time ◽  
Graphics Processing Units ◽  
Large Scale ◽  
Experimental Studies ◽  
Test Case ◽  
Cell Systems ◽  
Von Neumann ◽  
Agent Based ◽  
Cell Behaviors ◽  
Graphics Processing

Agent-Based Modeling has been recently recognized as a method for in-silico multi-scale modeling of biological cell systems. Agent-Based Models (ABMs) allow results from experimental studies of individual cell behaviors to be scaled into the macro-behavior of interacting cells in complex cell systems or tissues. Current generation ABM simulation toolkits are designed to work on serial von-Neumann architectures, which have poor scalability. The best systems can barely handle tens of thousands of agents in real-time. Considering that there are models for which mega-scale populations have significantly different emergent behaviors than smaller population sizes, it is important to have the ability to model such large scale models in real-time. In this paper we present a new framework for simulating ABMs on programmable graphics processing units (GPUs). Novel algorithms and data-structures have been developed for agent-state representation, agent motion, and replication. As a test case, we have implemented an abstracted version of the Systematic Inflammatory Response System (SIRS) ABM. Compared to the original implementation on the NetLogo system, our implementation can handle an agent population that is over three orders of magnitude larger with close to 40 updates/sec. We believe that our system is the only one of its kind that is capable of efficiently handling realistic problem sizes in biological simulations.

Parallelization and sustainability of distributed genetic algorithms on many-core processors

2014 ◽  
Vol 7 (1) ◽  
pp. 2-23 ◽  
Author(s):  
Yuji Sato ◽  
Mikiko Sato
Keyword(s):  
Graphics Processing Units ◽  
Fault Tolerant ◽  
Optimal Solution ◽  
New Approach ◽  
Content Type ◽  
Distributed Genetic Algorithms ◽  
Distributed Genetic Algorithm ◽  
Many Core ◽  
Graphics Processing ◽  
Application Programs

Purpose – The purpose of this paper is to propose a fault-tolerant technology for increasing the durability of application programs when evolutionary computation is performed by fast parallel processing on many-core processors such as graphics processing units (GPUs) and multi-core processors (MCPs). Design/methodology/approach – For distributed genetic algorithm (GA) models, the paper proposes a method where an island's ID number is added to the header of data transferred by this island for use in fault detection. Findings – The paper has shown that the processing time of the proposed idea is practically negligible in applications and also shown that an optimal solution can be obtained even with a single stuck-at fault or a transient fault, and that increasing the number of parallel threads makes the system less susceptible to faults. Originality/value – The study described in this paper is a new approach to increase the sustainability of application program using distributed GA on GPUs and MCPs.

scholarly journals Real-time Bidding for Time Constrained Impression Contracts in First and Second Price Auctions - Theory and Algorithms

2021 ◽  
Vol 5 (3) ◽  
pp. 1-37
Author(s):  
Ryan J. Kinnear ◽  
Ravi R. Mazumdar ◽  
Peter Marbach
Keyword(s):  
Real Time ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Piecewise Constant Function ◽  
Convex Optimization Problem ◽  
Piecewise Constant ◽  
Infinite Dimensional ◽  
Optimal Behavior ◽  
Price Auction ◽  
Second Price Auctions

We study the optimal bids and allocations in a real-time auction for heterogeneous items subject to the requirement that specified collections of items of given types be acquired within given time constraints. The problem is cast as a continuous time optimization problem that can, under certain weak assumptions, be reduced to a convex optimization problem. Focusing on the standard first and second price auctions, we first show, using convex duality, that the optimal (infinite dimensional) bidding policy can be represented by a single finite vector of so-called ''pseudo-bids''. Using this result we are able to show that the optimal solution in the second price case turns out to be a very simple piecewise constant function of time. This contrasts with the first price case that is more complicated. Despite the fact that the optimal solution for the first price auction is genuinely dynamic, we show that there remains a close connection between the two cases and that, empirically, there is almost no difference between optimal behavior in either setting. This suggests that it is adequate to bid in a first price auction as if it were in fact second price. Finally, we detail methods for implementing our bidding policies in practice with further numerical simulations illustrating the performance.

scholarly journals Data Rate Maximization in RIS-Assisted D2D Communication with Transceiver Hardware Impairments

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 200
Author(s):  
Hongxia Zheng ◽  
Chiya Zhang ◽  
Yatao Yang ◽  
Xingquan Li ◽  
Chunlong He
Keyword(s):  
Optimization Problem ◽  
Original Problem ◽  
Optimal Solution ◽  
Base Station ◽  
D2d Communication ◽  
Convex Optimization Problem ◽  
Hardware Impairments ◽  
Convex Problems ◽  
Rate Maximization ◽  
Power Limit

We maximize the transmit rate of device-to-device (D2D) in a reconfigurable intelligent surface (RIS) assisted D2D communication system by satisfying the unit-modulus constraints of reflectin elements, the transmit power limit of base station (BS) and the transmitter in a D2D pair. Since it is a non-convex optimization problem, the block coordinate descent (BCD) technique is adopted to decouple this problem into three subproblems. Then, the non-convex subproblems are approximated into convex problems by using successive convex approximation (SCA) and penalty convex-concave procedure (CCP) techniques. Finally, the optimal solution of original problem is obtained by iteratively optimizing the subproblems. Simulation results reveal the validity of the algorithm that we proposed to solve the optimization problem and illustrate the effectiveness of RIS to improve the transmit rate of the D2D pair even with hardware impairments.

scholarly journals Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units

2016 ◽  
Vol 41 ◽  
pp. 290-304 ◽  
Author(s):  
Adriane B.S. Serapião ◽  
Guilherme S. Corrêa ◽  
Felipe B. Gonçalves ◽  
Veronica O. Carvalho
Keyword(s):  
Data Clustering ◽  
Graphics Processing Units ◽  
Search Algorithm ◽  
Fish School ◽  
Graphics Processing

scholarly journals Parallel genetic algorithms on the graphics processing units using island model and simulated annealing

2017 ◽  
Vol 9 (7) ◽  
pp. 168781401770741 ◽  
Author(s):  
Cheng-Chieh Li ◽  
Chu-Hsing Lin ◽  
Jung-Chun Liu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Graphics Processing Units ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Optimization Method ◽  
Approximate Algorithm ◽  
Parallel Genetic Algorithms ◽  
Graphics Processing

To solve a non-deterministic polynomial-hard problem, we can adopt an approximate algorithm for finding the near-optimal solution to reduce the execution time. Although this approach can come up with solutions much faster than brute-force methods, the downside of it is that only approximate solutions are found in most situations. The genetic algorithm is a global search heuristic and optimization method. Initially, genetic algorithms have many shortcomings, such as premature convergence and the tendency to converge toward local optimal solutions; hence, many parallel genetic algorithms are proposed to solve these problems. Currently, there exist many literatures on parallel genetic algorithms. Also, a variety of parallel genetic algorithms have been derived. This study mainly uses the advantages of graphics processing units, which has a large number of cores, and identifies optimized algorithms suitable for computation in single instruction, multiple data architecture of graphics processing units. Furthermore, the parallel simulated annealing method and spheroidizing annealing are also used to enhance performance of the parallel genetic algorithm.

scholarly journals Vision-Based Sensing Systems for Autonomous Driving: Centralized or Decentralized?

2021 ◽  
Vol 33 (3) ◽  
pp. 686-697
Author(s):  
Manato Hirabayashi ◽  
Yukihiro Saito ◽  
Kosuke Murakami ◽  
Akihito Ohsato ◽  
Shinpei Kato ◽  
...  
Keyword(s):  
Power Consumption ◽  
Graphics Processing Units ◽  
Autonomous Driving ◽  
Prototype System ◽  
Sensing Systems ◽  
Network Load ◽  
Host Machine ◽  
Single Host ◽  
Taking Charge ◽  
Graphics Processing

The perception of the surrounding circumstances is an essential task for fully autonomous driving systems, but its high computational and network loads typically impede a single host machine from taking charge of the systems. Decentralized processing is a candidate to decrease such loads; however, it has not been clear that this approach fulfills the requirements of onboard systems, including low latency and low power consumption. Embedded oriented graphics processing units (GPUs) are attracting great interest because they provide massively parallel computation capacity with lower power consumption compared to traditional GPUs. This study explored the effects of decentralized processing on autonomous driving using embedded oriented GPUs as decentralized units. We implemented a prototype system that off-loaded image-based object detection tasks onto embedded oriented GPUs to clarify the effects of decentralized processing. The results of experimental evaluation demonstrated that decentralized processing and network quantization achieved approximately 27 ms delay between the feeding of an image and the arrival of detection results to the host as well as approximately 7 W power consumption on each GPU and network load degradation in orders of magnitude. Judging from these results, we concluded that decentralized processing could be a promising approach to decrease processing latency, network load, and power consumption toward the deployment of autonomous driving systems.

scholarly journals Method for Adaptation of Algorithms to GPU Architecture

2021 ◽  
Author(s):  
Vadim Bulavintsev ◽  
Dmitry Zhdanov
Keyword(s):  
Graphics Processing Units ◽  
Search Algorithm ◽  
Boolean Satisfiability ◽  
Control Flow ◽  
Code Optimization ◽  
Search Performance ◽  
Backtracking Search ◽  
Boolean Satisfiability Problem ◽  
Graphics Processing ◽  
Gpu Architecture

We propose a generalized method for adapting and optimizing algorithms for efficient execution on modern graphics processing units (GPU). The method consists of several steps. First, build a control flow graph (CFG) of the algorithm. Next, transform the CFG into a tree of loops and merge non-parallelizable loops into parallelizable ones. Finally, map the resulting loops tree to the tree of GPU computational units, unrolling the algorithm’s loops as necessary for the match. The mapping should be performed bottom-up, from the lowest GPU architecture levels to the highest ones, to minimize off-chip memory access and maximize register file usage. The method provides programmer with a convenient and robust mental framework and strategy for GPU code optimization. We demonstrate the method by adapting to a GPU the DPLL backtracking search algorithm for solving the Boolean satisfiability problem (SAT). The resulting GPU version of DPLL outperforms the CPU version in raw tree search performance sixfold for regular Boolean satisfiability problems and twofold for irregular ones.

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