Engineering topics
Publications and source records attributed to Giedt, Joel.
We conducted theoretical studies in high energy physics beyond the standard model, using lattice gauge theory techniques, with a particular emphasis on supersymmetric fields theories. There are many reasons for formulating and studying such theories using this first principles approach, with the goal in mind of repeating the successes of lattice quantum chromodynamics—which are quite substantial.
In this paper we use lattice simulation to study four dimensional $\mathcal{N}$ = 4 super Yang-Mills (SYM) theory. We have focused on the three color theory on lattices of size 12 4 and for ’t Hooft couplings up to λ = 40.0. Our lattice action is based on a discretization of the Marcus or GL twist of $\mathcal{N}$ = 4 SYM and retains one exact supersymmetry for non-zero lattice spacing. We show that lattice theory exists in a single non-Abelian Coulomb phase for all ’t Hooft couplings. Furthermore the static potential we obtain from correlators of Polyakov lines is in good agreement with that obtained from holography — specifically the potential has a Coulombic form with a coefficent that varies as the square root of the ’t Hooft coupling.
When simulating a lattice system near its critical temperature, local algorithms for modeling the system’s evolution can introduce very large autocorrelation times into sampled data. Here, this critical slowing down places restrictions on the analysis that can be completed in a timely manner of the behavior of systems around the critical point. Because it is often desirable to study such systems around this point, a new algorithm must be introduced. Therefore, we turn to cluster algorithms, such as the Swendsen–Wang algorithm and the Wolff clustering algorithm. They incorporate global updates which generate new lattice configurations with little correlation to previous states, even near the critical point. We look to accelerate the rate at which these algorithm are capable of running by implementing and benchmarking a parallel implementation of each algorithm designed to run on GPUs under NVIDIA’s CUDA framework. A 17 and 90 fold increase in the computational rate was, respectively, experienced when measured against the equivalent algorithm implemented in serial code.