Alessio Guerri

Communication-aware allocation and scheduling framework for stream-oriented multi-processor systems-on-chip

This paper proposes a complete allocation and scheduling framework, where an MPSoC virtual platform is used to accurately derive input parameters, validate abstract models of system components and assess constraint satisfaction and objective function optimization. The optimizer implements an efficient and exact approach to allocation and scheduling based on problem decomposition. The allocation subproblem is solved through integer programming while the scheduling one through constraint programming. The two solvers can interact by means of no-good generation, thus building an iterative procedure which has been proven to converge to the optimal solution. Experimental results show significant speedups w.r.t. pure IP and CP exact solution strategies as well as high accuracy with respect to cycle accurate functional simulation. A case study further demonstrates the practical viability of our framework for real-life systems and applications

Allocation and scheduling for MPSoCs via decomposition and no-good generation

This paper describes an efficient, complete approach for solving a complex allocation and scheduling problem for Multi-Processor System-on-Chip (MPSoC). Given a throughput constraint for a target application characterized as a task graph annotated with computation, communication and storage requirements, we compute an allocation and schedule which minimizes communication cost first, and then the makespan given the minimal communication cost. Our approach is based on problem decomposition where the allocation is solved through an Integer Programming solver, while the scheduling through a Constraint Programming solver. The two solvers are interleaved and their interaction regulated by no-good generation. Experimental results show speedups of orders of magnitude w.r.t. pure IP and CP solution strategies.

A fast and accurate technique for mapping parallel applications on stream-oriented MPSoC platforms with communication awareness

The problem of allocating and scheduling precedence-constrained tasks on the processors of a distributed real-time system is NP-hard. As such, it has been traditionally tackled by means of heuristics, which provide only approximate or near-optimal solutions. This paper proposes a complete allocation and scheduling framework, and deploys an MPSoC virtual platform to validate the accuracy of modelling assumptions. The optimizer implements an efficient and exact approach to the mapping problem based on a decomposition strategy. The allocation subproblem is solved through Integer Programming (IP) while the scheduling one through Constraint Programming (CP). The two solvers interact by means of an iterative procedure which has been proven to converge to the optimal solution. Experimental results show significant speed-ups w.r.t. pure IP and CP exact solution strategies as well as high accuracy with respect to cycle-accurate functional simulation. Two case studies further demonstrate the practical viability of our framework for real-life applications.

Allocation, scheduling and voltage scaling on energy aware MPSoCs

In this paper we introduce a complex allocation and scheduling problem for variable voltage Multi-Processor System-on-Chip (MPSoC) platforms. We propose a methodology to formulate and solve to optimality the allocation, scheduling and discrete voltage selection problem, minimizing the system energy dissipation and the overhead for frequency switching. Our approach is based on the Logic Benders decomposition technique where the allocation is solved through an Integer Programming solver, and the scheduling through a Constraint Programming solver. The two solvers are interleaved and their interaction regulated by cutting plane generation. The objective function depends on both master and sub-problem variables. We demonstrate the efficiency of our approach on a set of realistic instances.

Making Choices using Structure at the Instance Level within a Case Based Reasoning Framework

We describe using Case Based Reasoning to explore structure at the instance level as a means to distinguish whether to use CP or IP to solve instances of the Bid Evaluation Problem.

Bid evaluation in combinatorial auctions: optimization and learning

In combinatorial auctions bidders can post bids on groups of items. The problem of selecting the winning bids, called Winner Determination Problem, is NP-hard. In this paper, we consider an interes...

CP-IP techniques for the bid evaluation in Combinatorial auctions

Combinatorial auctions are an important e-commerce application where bidders can bid on combinations of items. The problem of selecting the best bids that cover all items, i.e., the Winner Determination Problem (WDP), is NP-hard. In this paper we consider the time constrained variant of this problem, that is the Bid Evaluation Problem (BEP) where temporal windows and precedence constraints are associated to each task in the bid. We propose different algorithms based on CP, IP and a hybrid approach based on both of them. We show that even the simplest pure CP based approach outperforms the only existing approach. We selected a set of algorithms which do not dominate each other. We identified a set of instance-dependent structural features that enable to select the best class of algorithms to apply. This is the first step toward an automatic algorithm selection in algorithm portfolios.

Machine learning for portfolio selection using structure at the instance level

Many combinatorial optimization problems do not have a clear structure, may present many side constraints, and may include subproblems. In addition, different instances within the same domain can have different structure and characteristics. As a consequence it is commonplace that a single algorithm is not the best performer on every problem instance. We consider an algorithm portfolio approach to try to help us select the best algorithm for a given problem instance. Our purpose is twofold: firstly, to show that structure at the instance level is tightly connected to algorithm performance, and secondly to demonstrate that different machine learning and modelling methodologies, specifically Decision Trees (DT), Case Based Reasoning (CBR) and Multinomial Logistic Regression (MLR), can be used to perform effective algorithm portfolio selection. We test our claims by applying the above mentioned techniques to a large set of instances of the Bid Evaluation Problem (BEP) in Combinatorial Auctions. A BEP consists of a Winner Determination Problem (a well-known NP-hard problem best solved by a IP-based approach), and additional temporal information and precedence constraints (which favour a CP-based approach). We solved the BEP instances using a set of different algorithms. We observed that two algorithms; one IP-based and the other a hybrid combining both CP and IP elements, outperformed all the others on all instances. Hence we divided the instances into 2 classes based on which of these 2 algorithms solves them best. In order to perform our analysis we extract a set of structure-based features, that are cheap to determine, from each instance. We apply the Machine Learning methodologies using the extracted features as input data and the best algorithms as prediction classes. This work has received support from Science Foundation Ireland under Grant 00/PI.1/C075. This work was partially supported by the SOCS project, funded by the CEC, contract IST-2001-32530.