Integer linear programming vs. graph-based methods in code generation

A common characterictic of many applications is that they are aimed at the high-volume consumer market, which is extremely cost-sensitive. However many of them impose stringent performance demands on the underlying system. Therefore the code generation must take into account the restrictions and features given by the target architecture while satisfying these performance demands. High-level language compilers often are unable to generate code meeting these requirements. One reason is the phase coupling problem between instruction scheduling and register allocation. Many compilers perform these tasks separately with each phase ignorant of the require- ments of the other. Commonly, each task is accomplished by using heuristic methods. As the goals of the two phases often conflict, whichever phase is performed first imposes constraints on the other, sometimes producing inefficient code. Integer linear programming (ILP) provides an integrated approach to the combined instruction scheduling and register allocation problem. This way, optimal solutions can be found - albeit at the cost of high compilation times. In our experiments, we considered as target processor the 32-bit DSP ADSP-2106x. We have examined two different ILP formulations and compared them with conventional approaches including list scheduling and the critical path method. Moreover, we have investigated approximations based on the ILP formulations; this way, compilation time can be reduced considerably while still producing near-optimal results. From the results of our implementation, we have concluded that integrating ILP formulations in conventional global algorithms is a promising method for generating high-quality code

General advanced job shop scheduling approach

The development of this thesis aims to design a new approach for solving production planning and scheduling in the process industries in such a way to be adaptable to any manufacturing plant, the description of which would have to be previously provided together with a series of ordered jobs. The planning and scheduling solving is concerned with the allocation over time of scarce resources between competing activities to meet a given set of requirements with an efficient organization. But, things get complicated as larger the scale of the problem is, i.e. as more resources, activities and requirements are involved. That is why the orientation of the work is focused on an innovative method in the style of Artificial Intelligence, by means of an automated process seeking to converge to a predefined objective. Although this research object has been studied since the middle of the last century, major breakthroughs were not achieved until the emergence of high-performance computing technologies; since by nature these are combinatorial problems which, the larger the scale, the more exploration they require to find some optimal. In addition, most of the last years related articles has been focused on solution approaches based on mathematical programming techniques, and it is important to note that there are other solution methods for dealing with this kind of problems. These methods can be used either as alternative methods, or as methods that can be combined with mathematical programming models, like the one proposed in this documen

Capacity planning and stochastic scheduling in grid computing environments

Imperial Users onl

Integer linear programming vs. graph-based methods in code generation

A common characterictic of many applications is that they are aimed at the high-volume consumer market, which is extremely cost-sensitive. However many of them impose stringent performance demands on the underlying system. Therefore the code generation must take into account the restrictions and features given by the target architecture while satisfying these performance demands. High-level language compilers often are unable to generate code meeting these requirements. One reason is the phase coupling problem between instruction scheduling and register allocation. Many compilers perform these tasks separately with each phase ignorant of the require- ments of the other. Commonly, each task is accomplished by using heuristic methods. As the goals of the two phases often conflict, whichever phase is performed first imposes constraints on the other, sometimes producing inefficient code. Integer linear programming (ILP) provides an integrated approach to the combined instruction scheduling and register allocation problem. This way, optimal solutions can be found - albeit at the cost of high compilation times. In our experiments, we considered as target processor the 32-bit DSP ADSP-2106x. We have examined two different ILP formulations and compared them with conventional approaches including list scheduling and the critical path method. Moreover, we have investigated approximations based on the ILP formulations; this way, compilation time can be reduced considerably while still producing near-optimal results. From the results of our implementation, we have concluded that integrating ILP formulations in conventional global algorithms is a promising method for generating high-quality code

Reading "all about" computerization: five common genres of social analysis

This paper examines unstated, but critical, social assumptions which underlie analyses of computerization. It focuses on the popular, professional and scholarly literature which claims to describe the actual nature of computerization, the character of computer use, and the social choices and changes that result from computerization. This literature can be usefully segmented five ideal type genres: utopian, anti-utopian, social realism, social theory, and analytical reduction. Each genre is characterized and illustrated. The strengths and weaknesses of each genre are described. In the 1990s, there will be a large market for social analyses of computerization. Utopian analyses are most likely to domínate the popular and professional discourse. The empirically oriented accounts of social realism, social theory and analytical reduction, are likely to be much less common and also less commonly seen and read by computer professionals and policymakers. These genres are relatively subtle, portray a more ambiguous world, and have less rhetorical power to capture the imagination of readers. Even though they are more scientific, these empirically anchored genres don't seem to appeal to many scientists and engineers. It is ironic that computing -- often portrayed as an instrument of knowledge -- is primarily the subject of discourses whose knowledge claims are most suspect. Conversely, the discourses whose claims as valid knowledge are strongest seems to have much less appeal in the mass media and technological communities

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008