Dynamic scheduling: integrating schedule risk analysis with earned value management

The topic of this paper is dynamic project scheduling to illustrate that project scheduling is a dynamic process that involves a continuous stream of changes and is a never ending process to support decisions that need to be made along the life of the project. The focus of this paper lies on three crucial dimensions of dynamic scheduling which can be briefly outlined along the following lines: (i) Baseline scheduling to construct a timetable that provides a start and end date for each project activity, taking activity relations, resource constraints and other project characteristics into account, and aiming to reach a certain scheduling objective, (ii) risk analysis to analyze the strengths and weaknesses of your project schedule in order to obtain information about the schedule sensitivity and the possible changes that undoubtedly occur during project progress and (iii) project control to measure the (time and cost) performance of a project during its progress and use the information obtained during the scheduling and risk analysis steps to monitor and update the project and to take corrective actions in case of problems. The focus of the current paper is on the importance and crucial role of the baseline scheduling component for the two other components, and the integration of the schedule risk and project control component in order to support a better corrective action decision making when the project is in trouble

On Multi-dimensional Packing Problems

We study the approximability of multi-dimensional generalizations of three classical packing problems: multiprocessor scheduling, bin packing, and the knapsack problem. Specifically, we study the vector scheduling problem, its dual problem, namely, the vector bin packing problem, and a class of packing integer programs. The vector scheduling problem is to schedule n d-dimensional tasks on m machines such that the maximum load over all dimensions and all machines is minimized. The vector bin packing problem, on the other hand, seeks to minimize the number of bins needed to schedule all n tasks such that the maximum load on any dimension accross all bins is bounded by a fixed quantity, say 1. Such problems naturally arise when scheduling tasks that have multiple resource requirements. Finally, packing integer programs capture a core problem that directly relates to both vector scheduling and vector bin packing, namely, the problem of packing a miximum number of vectors in a single bin of unit height. We obtain a variety of new algorithmic as well as inapproximability results for these three problems

Work Design for Flexible Work Scheduling: Barriers and Gender Implications

This paper investigates the assumptions underlying three important dimensions of work design, place, distance and time, and their significant linkages to flexible work scheduling and travel behaviour. Work design, defined as the interrelationship of work tasks, workers and workplace routines, moderates the relationship between distributed work, flexible work scheduling and travel behaviour. Models of work design based on conventional views of place, distance and time are restrictive in supporting the potential of flexible work scheduling. Work practices that assume work is conducted only in the workplace (place), during standard work time (time) in the proximity of coworkers and managers (distance) do not, in the main, support flexible work scheduling. This paper considers the broader framework of organisational change and work design from the employer perspective in the context of distributed work and diffusion of communications technology, and its influence on flexible work scheduling

Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code

This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. Tiramisu introduces a scheduling language with novel extensions to explicitly manage the complexities that arise when targeting these systems. The framework is designed for the areas of image processing, stencils, linear algebra and deep learning. Tiramisu has two main features: it relies on a flexible representation based on the polyhedral model and it has a rich scheduling language allowing fine-grained control of optimizations. Tiramisu uses a four-level intermediate representation that allows full separation between the algorithms, loop transformations, data layouts, and communication. This separation simplifies targeting multiple hardware architectures with the same algorithm. We evaluate Tiramisu by writing a set of image processing, deep learning, and linear algebra benchmarks and compare them with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu matches or outperforms existing compilers and libraries on different hardware architectures, including multicore CPUs, GPUs, and distributed machines.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0041

Hypercube algorithms on mesh connected multicomputers

A new methodology named CALMANT (CC-cube Algorithms on Meshes and Tori) for mapping a type of algorithm that we call CC-cube algorithm onto multicomputers with hypercube, mesh, or torus interconnection topology is proposed. This methodology is suitable when the initial problem can be expressed as a set of processes that communicate through a hypercube topology (a CC-cube algorithm). There are many important algorithms that fit into the CC-cube type. CALMANT is based on three different techniques: (a) the standard embedding to assign the processes of the algorithm to the nodes of the mesh multicomputer; (b) the communication pipelining technique to increase the level of communication parallelism inherent in the CC-cube algorithms; and (c) optimal message-scheduling algorithms proposed in this work in order to avoid conflicts and minimizing in this way the communication time. Although CALMANT is proposed for multicomputers with different interconnection network topologies, the paper only focuses on the particular case of meshes.Peer ReviewedPostprint (published version