| Resumo: | Abstract Applying parallelism to constraint solving seems a promising approach and it has been done with varying degrees of success. Early attempts to parallelize constraint propagation, which constitutes the core of traditional interleaved propagation and search constraint solving, were hindered by its essentially sequential nature. Recently, parallelization efforts have focussed mainly on the search part of constraint solving. A particular source of parallelism has become pervasive, in the guise of GPUs, able to run thousands of parallel threads, and they have naturally drawn the attention of researchers in parallel constraint solving. This thesis addresses the challenges faced when using multiple devices for constraint solving, especially GPUs, such as deciding on the appropriate level of parallelism to employ, load balancing and inter-device communication. To overcome these challenges new techniques were implemented in a new constraint solver, named Parallel Heterogeneous Architecture Constraint Toolkit (PHACT), which allows to use one or more CPUs, GPUs, Intel Many Integrated Cores (MIC) and any other device compatible with OpenCL to solve a constraint problem. Several tests were made to measure the capabilities of some GPUs to solve constraint problems, and the conclusions of these tests are described in this thesis. PHACT’s architecture is presented and its performance was measured in each one of five machines, comprising eleven CPUs, six GPUs and two MICs. The tests were made using 10 constraint satisfaction problems, consisting in counting all the solutions, finding one solution or optimizing. Each of the problems has been instantiated with up to three different dimensions. PHACT’s performance was also compared with the ones of Gecode, Choco and OR-Tools. In the end, these tests allowed to detect which techniques implemented in PHACT were already achieving the expected results, and to point changes that may improve PHACT’s performance.
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