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Predictive modeling in a polyhedral optimization space

Eunjung Park, Louis-Noel Pouche, John Cavazos, Albert Cohen, P. Sadayappan
2011 International Symposium on Code Generation and Optimization (CGO 2011)  
in the polyhedral space.  ...  In this paper, we propose to solve the polyhedral optimization selection problem using machine learning models.  ...  Acknowledgments: This work was funded in part by the U.S.  ... 
doi:10.1109/cgo.2011.5764680 dblp:conf/cgo/ParkPCCS11 fatcat:4jf4y3zuwza37bpdp6je5yyrkm

Predictive Modeling in a Polyhedral Optimization Space

Eunjung Park, John Cavazos, Louis-Noël Pouchet, Cédric Bastoul, Albert Cohen, P. Sadayappan
2013 International journal of parallel programming  
in the polyhedral space.  ...  In this paper, we propose to solve the polyhedral optimization selection problem using machine learning models.  ...  Acknowledgments: This work was funded in part by the U.S.  ... 
doi:10.1007/s10766-013-0241-1 fatcat:x3slz5gqlfdzzhu3jnlyfetjhy

Inverse parametric convex programming problems via convex liftings

N.A. Nguyen, S. Olaru, P. Rodriguez-Ayerbe, M. Hovd, I. Necoara
2014 IFAC Proceedings Volumes  
Finally, we show that the theoretical results has a practical interest in Model Predictive Control (MPC) design.  ...  It is shown that any linear Model Predictive Controller can be obtained through a reformulated MPC problem with control horizon equal to two prediction steps.  ...  RELATED MODEL PREDICTIVE CONTROL PROBLEMS In this section, the related model predictive control problems will be presented as applications of the aforementioned constructive procedure of inverse optimal  ... 
doi:10.3182/20140824-6-za-1003.02364 fatcat:r67xxob3zngdfiadlhvlury7ky

Polyhedral-Model Guided Loop-Nest Auto-Vectorization

Konrad Trifunovic, Dorit Nuzman, Albert Cohen, Ayal Zaks, Ira Rosen
2009 2009 18th International Conference on Parallel Architectures and Compilation Techniques  
Our work demonstrates the feasibility and benefit of tuning the polyhedral model in the context of vectorization.  ...  Optimizing compilers apply numerous interdependent optimizations, leading to the notoriously difficult phase-ordering problem -that of deciding which transformations to apply and in which order.  ...  A. Polyhedral Compilation A well known alternative approach, facilitating complex loop transformations, represents programs in the polyhedral model.  ... 
doi:10.1109/pact.2009.18 dblp:conf/IEEEpact/TrifunovicNCZR09 fatcat:gm7mb2senfbbjjqw2n7miburja

ROBUST MODEL PREDICTIVE CONTROL FOR SWITCHED PIECEWISE LINEAR HYBRID SYSTEMS

Jean THOMAS
2006 JES. Journal of Engineering Sciences  
A model predictive control law derived from a quadratic cost function minimization is further examined as a simple and fast sub-optimal robust control.  ...  Checking attainability and calculating the state space regions for which a robust control is assured despite the uncertainty is performed using a polyhedral approach.  ...  REACHABILITY AND ATTAINABILITY; A POLYHEDRAL APPROACH Let consider the region 1 ,  k k R , as a target region in the global state space X .  ... 
doi:10.21608/jesaun.2006.111092 fatcat:drx7t2zqgfepfbgslcb74m63be

The Polyhedral Model of Nonlinear Loops

Aravind Sukumaran-Rajam, Philippe Clauss
2015 ACM Transactions on Architecture and Code Optimization (TACO)  
The polyhedral model is a wellknown mathematical model to analyze and optimize loop nests. The current state-of-art tools limit the application of the polyhedral model to static control codes.  ...  Apollo (Automatic POLyhedral Loop Optimizer) is a framework that goes one step beyond and applies the polyhedral model dynamically by using TLS.  ...  In comparison, Apollo features a linear prediction model using interpolation and regression; applies optimizing polyhedral transformation on-the-fly, aiming for automatic data locality optimization and  ... 
doi:10.1145/2838734 fatcat:zmysr5it2vd4lnmagdhkzxvowq

Analytical Cost Metrics : Days of Future Past [article]

Nirmal Prajapati, Sanjay Rajopadhye, Hristo Djidjev
2018 arXiv   pre-print
We propose the following strategy to solve the problem: (i) Models - Develop accurate analytical models (e.g. execution time, energy, silicon area) to predict the cost of executing a given program, and  ...  (ii) Complete System Design - Simultaneously optimize all the cost models for the programs (computational problems) to obtain the most time/area/power/energy efficient solution.  ...  We add performance models to this design space and provide a unified view of the optimization space. Figure 1.3 shows this view (more details in Section 2.2).  ... 
arXiv:1802.01957v1 fatcat:r6lajnt75zb4xahkznt5gb4wx4

An efficient application of particle swarm optimization in model predictive control of constrained two-tank system

Ahmad Kia Kojouri, Javad Mashayekhi Fard
2022 International Journal of Power Electronics and Drive Systems (IJPEDS)  
In this paper, we present the efficiency of particle swarm optimization (PSO) in optimal control of a two-tank system modeled as piecewise affine.  ...  <span>Despite all the model predictive control (MPC) based solution advantages such as a guarantee of stability, the main disadvantage such as an exponential growth of the number of the polyhedral region  ...  A practical method in designing controllers of nonlinear systems is optimal control concepts in constrained and non-constrained processes by linear discrete-time models in the form of state space.  ... 
doi:10.11591/ijece.v12i4.pp3540-3550 fatcat:lvqe63gwjbhktari56luetovoa

Stable Frank–Kasper phases of self-assembled, soft matter spheres

Abhiram Reddy, Michael B. Buckley, Akash Arora, Frank S. Bates, Kevin D. Dorfman, Gregory M. Grason
2018 Proceedings of the National Academy of Sciences of the United States of America  
The relative stability of FK lattices is studied first using a diblock foam model with unconstrained particle volumes and shapes, which correctly predicts not only the equilibrium σ lattice, but also the  ...  These findings shed new light on the role of volume exchange on the formation of distinct FK phases in copolymers, and suggest a paradigm for formation of FK phases in soft matter systems in which unequal  ...  We define a second parameter, ν(X ), that measures asymmetry of unequal volumes enclosed within A/B interfaces predicted by SCFT, relative to the volume asymmetry predicted by polyhedral cells of DFM for  ... 
doi:10.1073/pnas.1809655115 pmid:30249659 pmcid:PMC6187128 fatcat:7nhplqp3cben7b5okziml32leq

An Improved Machine Learning Approach for Selecting a Polyhedral Model Transformation [chapter]

Ray Ruvinskiy, Peter van Beek
2015 Lecture Notes in Computer Science  
The polyhedral model allows the automatic generation of loop nest transformations that are semantically equivalent to the original.  ...  In this paper, we present an improved machine learning approach to select the best transformation.  ...  [13] use regression models to predict the runtime of a polyhedral transformation of a source program and so select a transformation.  ... 
doi:10.1007/978-3-319-18356-5_9 fatcat:i6ogvruoqbhhdjnrb6pdj5b6vq

Robust Model Predictive Control for Piecewise Affine Systems Subject To Bounded Disturbances [chapter]

J. Thomas, S. Olaru, J. Buisson, D. Dumur
2006 Analysis and Design of Hybrid Systems 2006  
A model predictive control law derived from a quadratic cost function minimization is further examined as a fast sub-optimal robust control.  ...  Checking attainability and calculating the state space regions for which a robust control is assured despite the disturbance is performed using a polyhedral approach.  ...  1 , > k k R as a target region in the global state space X .  ... 
doi:10.1016/b978-008044613-4.50060-4 fatcat:2pl6riorhbhf5im3dttfjh4wbm

Real-Time Optimal Control for Irregular Asteroid Landings Using Deep Neural Networks [article]

Lin Cheng, Zhenbo Wang, Yu Song, Fanghua Jiang
2019 arXiv   pre-print
control instructions in real time because there is no longer need to solve the optimal landing problems onboard.  ...  First, to significantly reduce the time consumption of gravity calculation, DNNs are used to approximate the irregular gravitational field of the asteroid based on the samples from a polyhedral method.  ...  Then, the trained network can replace the polyhedral item in the dynamical model, and the computational speed of predicting the gravity can be significantly improved.  ... 
arXiv:1901.02210v1 fatcat:vvrpmgkc5fd5pixqxgbjj4cju4

ROBUST MODEL PREDICTIVE CONTROL FOR PIECEWISE AFFINE SYSTEMS SUBJECT TO BOUNDED DISTURBANCES

J. Thomas, S. Olaru, J. Buisson, D. Dumur
2006 IFAC Proceedings Volumes  
A model predictive control law derived from a quadratic cost function minimization is further examined as a fast sub-optimal robust control.  ...  Checking attainability and calculating the state space regions for which a robust control is assured despite the disturbance is performed using a polyhedral approach.  ...  1 , > k k R as a target region in the global state space X .  ... 
doi:10.3182/20060607-3-it-3902.00061 fatcat:cwpufqpn25gstjjm3kpecvqe5e

Accelerating the BPMax Algorithm for RNA-RNA Interaction

Chiranjeb Mondal, Sanjay Rajopadhye
2021 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)  
Its high complexity (Θ(N 3 M 3 ) in time and Θ(N 2 M 2 ) in space) makes it both essential and a challenge to parallelize.  ...  We do this with a polyhedral code generation tool, AL P H AZ, which takes user-specified mapping directives and automatically generates optimized C code that enhances parallelism and locality.  ...  I am also grateful to him for accepting me as a student, spending his precious time with me, and guiding me through the basics of high-performance computing and polyhedral compilation.  ... 
doi:10.1109/ipdpsw52791.2021.00042 fatcat:2vvuozzyerg27fzxcz53xmywiy

Runtime multi-versioning and specialization inside a memoized speculative loop optimizer

Raquel Lazcano, Daniel Madroñal, Eduardo Juarez, Philippe Clauss
2020 Proceedings of the 29th International Conference on Compiler Construction  
In this paper, we propose a runtime framework that implements code multi-versioning and specialization to optimize and parallelize loop kernels that are invoked many times with varying parameters.  ...  They may also impact the validity of the parallelizing and optimizing polyhedral transformations that are applied on-the-fly.  ...  In [24] , Pouchet et al. propose advanced techniques using an heuristic and a genetic algorithm to traverse huge polyhedral optimization spaces.  ... 
doi:10.1145/3377555.3377886 dblp:conf/cc/LazcanoMJC20 fatcat:mc3knlmrdbdnbhj64bdbq2il6i
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