14,112 research outputs found
Transport or Store? Synthesizing Flow-based Microfluidic Biochips using Distributed Channel Storage
Flow-based microfluidic biochips have attracted much atten- tion in the EDA
community due to their miniaturized size and execution efficiency. Previous
research, however, still follows the traditional computing model with a
dedicated storage unit, which actually becomes a bottleneck of the performance
of bio- chips. In this paper, we propose the first architectural synthe- sis
framework considering distributed storage constructed tem- porarily from
transportation channels to cache fluid samples. Since distributed storage can
be accessed more efficiently than a dedicated storage unit and channels can
switch between the roles of transportation and storage easily, biochips with
this dis- tributed computing architecture can achieve a higher execution
efficiency even with fewer resources. Experimental results con- firm that the
execution efficiency of a bioassay can be improved by up to 28% while the
number of valves in the biochip can be reduced effectively.Comment: ACM/IEEE Design Automation Conference (DAC), June 201
Survey on Combinatorial Register Allocation and Instruction Scheduling
Register allocation (mapping variables to processor registers or memory) and
instruction scheduling (reordering instructions to increase instruction-level
parallelism) are essential tasks for generating efficient assembly code in a
compiler. In the last three decades, combinatorial optimization has emerged as
an alternative to traditional, heuristic algorithms for these two tasks.
Combinatorial optimization approaches can deliver optimal solutions according
to a model, can precisely capture trade-offs between conflicting decisions, and
are more flexible at the expense of increased compilation time.
This paper provides an exhaustive literature review and a classification of
combinatorial optimization approaches to register allocation and instruction
scheduling, with a focus on the techniques that are most applied in this
context: integer programming, constraint programming, partitioned Boolean
quadratic programming, and enumeration. Researchers in compilers and
combinatorial optimization can benefit from identifying developments, trends,
and challenges in the area; compiler practitioners may discern opportunities
and grasp the potential benefit of applying combinatorial optimization
The energy scheduling problem: Industrial case-study and constraint propagation techniques
This paper deals with production scheduling involving energy constraints, typically electrical energy.
We start by an industrial case-study for which we propose a two-step integer/constraint programming method. From the industrial problem we derive a generic problem,the Energy Scheduling Problem (EnSP). We propose an extension of specific resource constraint propagation techniques to efficiently prune the search space for EnSP solving. We also present a branching scheme to solve the problem via
tree search.Finally,computational results are provided
Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks
An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive h -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks
Scheduling Monotone Moldable Jobs in Linear Time
A moldable job is a job that can be executed on an arbitrary number of
processors, and whose processing time depends on the number of processors
allotted to it. A moldable job is monotone if its work doesn't decrease for an
increasing number of allotted processors. We consider the problem of scheduling
monotone moldable jobs to minimize the makespan.
We argue that for certain compact input encodings a polynomial algorithm has
a running time polynomial in n and log(m), where n is the number of jobs and m
is the number of machines. We describe how monotony of jobs can be used to
counteract the increased problem complexity that arises from compact encodings,
and give tight bounds on the approximability of the problem with compact
encoding: it is NP-hard to solve optimally, but admits a PTAS.
The main focus of this work are efficient approximation algorithms. We
describe different techniques to exploit the monotony of the jobs for better
running times, and present a (3/2+{\epsilon})-approximate algorithm whose
running time is polynomial in log(m) and 1/{\epsilon}, and only linear in the
number n of jobs
Continuous-Time Formulations for Multi-Mode Project Scheduling
This paper reviews compact continuous-time formulations for the multi-mode
resource-constrained project scheduling problem. Specifically, we first point
out a serious flaw in an existing start-end-event-based formulation owing to
inconsistent mode choices. We propose two options to formulate the missing
constraints and we consider an equivalent reformulation with sparser constraint
matrix. Second, we formulate an aggregate variant of an existing model that
relies on on-off-events and clarify the role of mode consistency issues in such
models. Third, we suggest two variants of an existing network flow formulation.
We enhance our models by adapting several techniques that have been used
previously, e.g., in cases with only a single mode. A large set of benchmark
instances from the literature provides the basis for an up-to-date and fair
computational study with an out-of-the-box solver package. We compare our
models against two models from the literature. Our experiments assert
confidently that network flow formulations prevail in the test bed, and they
provide a hint on why event-based models become less competitive in multi-mode
settings.Comment: 37 page
The Multimode Resource Constrained Project Scheduling Problem for Repetitive Activities in Construction Projects
[EN] In construction projects, resource availability might limit the implementation of ideal schedules. Especially, when repetitive activities are involved, traditional resource¿constrained project scheduling problem (RCPSP) models fail to allocate the resource consumption in an efficient manner. Besides, actual models only provide local optimal solutions and do not incorporate activity acceleration routines. To fulfill this gap, partially, a mathematical optimization model, the multimode RCPSP for repetitive activities in construction projects, is proposed and solved to optimality; it takes into account acceleration routines under real construction scenarios using spreadsheets. The article shows a complete computational experimentation over a real construction project, considering several scenarios of resource availabilities and continuity conditions. The model allows analyzing the resources efficiency indexes comparing them to resource consumptions, continuity of activities, and objective functions that reveal that fragmented activities do not provide better resource efficiency outcomes.This research was partially supported by the FAPA program of Universidad de Los Andes, Colombia (code P14.246922.005/01). The authors would also like to thank the research group of Construction Engineering and Management (INgeco) at Universidad de los Andes.García-Nieves, J.; Ponz-Tienda, JL.; Salcedo-Bernal, A.; Pellicer Armiñana, E. (2018). The Multimode Resource Constrained Project Scheduling Problem for Repetitive Activities in Construction Projects. Computer-Aided Civil and Infrastructure Engineering. 33(8):655-671. https://doi.org/10.1111/mice.12356S65567133
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