scholarly journals Answer Set versus Integer Linear Programming for Automatic Synthesis of Multiprocessor Systems from Real-Time Parallel Programs

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
Harold Ishebabi ◽  
Philipp Mahr ◽  
Christophe Bobda ◽  
Martin Gebser ◽  
Torsten Schaub
Keyword(s):  
Linear Programming ◽  
Real Time ◽  
Integer Linear Programming ◽  
Linear Equations ◽  
Parallel Programs ◽  
Design Parameters ◽  
Multiprocessor Systems ◽  
Task Mapping ◽  
Baseband Signal Processing ◽  
Answer Set

An automated design approach for multiprocessor systems on FPGAs is presented which customizes architectures for parallel programs by simultaneously solving the problems of task mapping, resource allocation, and scheduling. The latter considers effects of fixed-priority preemptive scheduling in order to guarantee real-time requirements, hence covering a broad spectrum of embedded applications. Being inherently a combinatorial optimization problem, the design space is modeled using linear equations that capture high-level design parameters. A comparison of two methods for solving resulting problem instances is then given. The intent is to study how well recent advances in propositional satisfiability (SAT) and thus Answer Set Programming (ASP) can be exploited to automate the design of flexible multiprocessor systems. Integer Linear Programming (ILP) is taken as a baseline, where architectures for IEEE 802.11g and WCDMA baseband signal processing are synthesized. ASP-based synthesis used a few seconds in the solver, faster by three orders of magnitude compared to ILP-based synthesis, thereby showing a great potential for solving difficult instances of the automated synthesis problem.

scholarly journals Task mapping and routing optimization for hard real-time Networks-on-Chip

2019 ◽  
Vol 8 (2) ◽  
pp. 414-421 ◽  
Author(s):  
M. Norazizi Sham Mohd Sayuti ◽  
Farida Hazwani Mohd Ridzuan ◽  
Zul Hilmi Abdullah
Keyword(s):  
Real Time ◽  
Optimization Technique ◽  
Network Routing ◽  
Design Parameters ◽  
Task Mapping ◽  
Evaluation Function ◽  
Worst Case ◽  
Networks On Chip ◽  
On Chip ◽  
Hard Real Time

Interference from high priority tasks and messages in a hard real-time Networks-on-Chip (NoC) create computation and communication delays. As the delays increase in number, maintaining the system’s schedulability become difficult. In order to overcome the problem, one way is to reduce interference in the NoC by changing task mapping and network routing. Some population-based heuristics evaluate the worst-case response times of tasks and messages based on the schedulability analysis, but requires a significant amount of optimization time to complete due to the complexity of the evaluation function. In this paper, we propose an optimization technique that explore both parameters simultaneously with the aim to meet the schedulability of the system, hence reducing the optimization time. One of the advantages from our approach is the unrepeated call to the evaluation function, which is unaddressed in the heuristics that configure design parameters in stages. The results show that a schedulable configuration can be found from the large design space.

scholarly journals Diet planning for humans using mixed-integer linear programming

1993 ◽  
Vol 70 (1) ◽  
pp. 27-35 ◽  
Author(s):  
David Sklan ◽  
Ilana Dariel
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Linear Equations ◽  
Minimum Cost ◽  
Mixed Integer ◽  
Programming Algorithm ◽  
Nutritional Requirements ◽  
Nutritional Requirement ◽  
Food Items

Human diet planning is generally carried out by selecting the food items or groups of food items to be used in the diet and then calculating the composition. If nutrient quantities do not reach the desired nutritional requirements, foods are exchanged or quantities altered and the composition recalculated. Iterations are repeated until a suitable diet is obtained. This procedure is cumbersome and slow and often leads to compromises in composition of the final diets. A computerized model, planning diets for humans at minimum cost while supplying all nutritional requirements, maintaining nutrient relationships and preserving eating practices is presented. This is based on a mixed-integer linear-programming algorithm. Linear equations were prepared for each nutritional requirement. To produce linear equations for relationships between nutrients, linear transformations were performed. Logical definitions for interactions such as the frequency of use of foods, relationships between exchange groups and the energy content of different meals were defined, and linear equations for these associations were written. Food items generally eaten in whole units were defined as integers. The use of this program is demonstrated for planning diets using a large selection of basic foods and for clinical situations where nutritional intervention is desirable. The system presented begins from a definition of the nutritional requirements and then plans the foods accordingly, and at minimum cost. This provides an accurate, efficient and versatile method of diet formulation.

Array Dataflow Analysis for Explicitly Parallel Programs

1997 ◽  
Vol 07 (02) ◽  
pp. 117-131 ◽  
Author(s):  
Jean-Fançois Collard ◽  
Martin Griebl
Keyword(s):  
Linear Programming ◽  
Data Structures ◽  
Integer Linear Programming ◽  
Parallel Programs ◽  
Dataflow Analysis ◽  
Flow Equations ◽  
Data Parallel ◽  
Task Parallel ◽  
Programming Paradigms ◽  
Or Task

This paper describes a dataflow analysis of array data structures for data-parallel and/or control- (or task-) parallel imperative languages. This analysis departs from previous work because it 1) simultaneously handles both parallel programming paradigms, and 2) does not rely on the usual iterative solving process of a set of data flow equations but extends array dataflow analysis based on integer linear programming, thus improving the precision of results.

scholarly journals An Energy Management System for the Control of Battery Storage in a Grid-Connected Microgrid Using Mixed Integer Linear Programming

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6212
Author(s):  
Marvin Barivure Sigalo ◽  
Ajit C. Pillai ◽  
Saptarshi Das ◽  
Mohammad Abusara
Keyword(s):  
Linear Programming ◽  
Real Time ◽  
Energy Management ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Management System ◽  
Mixed Integer ◽  
Energy Management System ◽  
Battery Charging ◽  
Load Demand

This paper proposes an energy management system (EMS) for battery storage systems in grid-connected microgrids. The battery charging/discharging power is determined such that the overall energy consumption cost is minimized, considering the variation in grid tariff, renewable power generation and load demand. The system is modeled as an economic load dispatch optimization problem over a 24 h horizon and solved using mixed integer linear programming (MILP). This formulation, therefore, requires knowledge of the expected renewable energy power production and load demand over the next 24 h. To achieve this, a long short-term memory (LSTM) network is proposed. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the EMS that benefits from using actual generation and demand data on the day. At each hour, the LSTM predicts generation and load data for the next 24 h, the dispatch problem is then solved and the battery charging or discharging command for only the first hour is applied in real-time. Real data are then used to update the LSTM input, and the process is repeated. Simulation results show that the proposed real-time strategy outperforms the offline optimization strategy, reducing the operating cost by 3.3%.

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