Modern MIP solvers exploit a rich arsenal of tools to attack hard problems, some of which include the solution of LP models to control the branching strategy (strong branching), the cut generation (lift-and-project), the heuristics (reduced costs), etc. As a matter of fact, it is well understood

and efficient communication on modern GPUs. Besides performance gains due to improved numerical computations, graphics algorithms benefit from this model in that the transfer of computation results to the graphics processor for display is avoided. We demonstrate the effectiveness of our approach by implementing

, such as soundness, completeness or termination, immediately carry over to the modern DPLL implementations with features such as backjumping or clause learning. We then extend the framework to Satisfiability Modulo background Theories (SMT) and use it to model several variants of the so-called lazy approach for SMT

Modern Mixed-Integer Programming (MIP) solvers exploit a rich arsenal of tools to attack hard problems. It is widely accepted by the OR community that the solution of very hard MIPs can take advantage from the solution of a series of time-consuming auxiliary Linear Programs (LPs) intended

Recent advances in AI planning technology have drastically improved the capabilities of modern planners. However, these advances have left research in domain specialization behind; many older specialization techniques are no longer applicable to modern planners. This paper explores how to automate

to traditional maxsat solvers, we also evaluate the use of a state-of-the-art Mixed Integer Program (mip) solver, cplex, for solving maxsat. mip solvers are the most popular technology for solving opti-mization problems and are also theoretically more powerful than sat solvers. In fact, we show that cplex

Beside impressive progresses made by SAT solvers over the last ten years, only few works tried to understand why Conflict Directed Clause Learning algorithms (CDCL) are so strong and efficient on most industrial applications. We report in this work a key observation of CDCL solvers behavior

successful prefetching schemes. Based on this guideline, we design three prefetching schemes, we automate the most widely applicable scheme (greedy prefetching) in an optimizing research compiler, and we evaluate the performance of all three schemes on a modern superscalar processor similar to the MIPS R

-purpose constraint solver, with an expressive input language based on the common constraint modelling device of matrix models. Focussing on matrix models supports a highly-optimised implementation, exploiting the properties of modern processors. This contrasts with current constraint toolkits, which, in order

. The sheer size of many modern physics and simulation problems has led to the development of massively parallel computers, and has sparked much research into developing algorithms for them. Parallelizing AMG is a difficult task, however. While much of the AMG method parallelizes readily, the process