Multithreaded Acceleration of 3D Mathematical Model for Ore Sintering

One of the widely used methods to accelerate a numerical solver is implementation of multithreading. The problem of thread allocation on-demand at runtime is latency, caused by periodical instantiation of threads. The article is devoted to parallelization of solver for 3D mathematical model of ore sintering, based on software threads reusing them during computation. Computational domain is equally shared among available threads. Each thread writes only to own data partition. A looped barrier is proposed for guaranteed synchronization of all threads after iteration. The method allows scaling performance without recompilation of the solver by using similar CPU with more cores. Measurement of solver performance with 220 nodes using different thread count confirms scalability around 95% for double and single precision arithmetics. Presented pictures of perspective view with three slices of temperature field show influence of heat loss from pallets walls. A cross section of temperature field in layer after 16 minutes of sintering is calculated with appearance of two high-temperature regions inside. Comparison of temperature field with literature data gives good correspondence. The computer model takes into account important chemical reactions, such as, coke burning, carbonate dissolution, water vaporization, as well as mass-heat transfer inside the sinter layer and can be used in metallurgical plants to increase effectiveness of sintering.

Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics

Optimisation has been with us since before the first humans opened their eyes to natural phenomena that inspire technological progress. Nowadays, it is quite hard to find a solver from the overpopulation of metaheuristics that properly deals with a given problem. This is even considered an additional problem. In this work, we propose a heuristic-based solver model for continuous optimisation problems by extending the existing concepts present in the literature. We name such solvers ‘unfolded’ metaheuristics (uMHs) since they comprise a heterogeneous sequence of simple heuristics obtained from delegating the control operator in the standard metaheuristic scheme to a high-level strategy. Therefore, we tackle the Metaheuristic Composition Optimisation Problem by tailoring a particular uMH that deals with a specific application. We prove the feasibility of this model via a two-fold experiment employing several continuous optimisation problems and a collection of diverse population-based operators with fixed dimensions from ten well-known metaheuristics in the literature. As a high-level strategy, we utilised a hyper-heuristic based on Simulated Annealing. Results demonstrate that our proposed approach represents a very reliable alternative with a low computational cost for tackling continuous optimisation problems with a tailored metaheuristic using a set of agents. We also study the implication of several parameters involved in the uMH model and their influence over the solver performance.

MULTILEVEL CMFD ACCELERATION METHODS FOR WHOLE CORE REACTOR SIMULATION IN VERA

Ongoing efforts are being made to improve the performance of MPACT as the deterministic neutron transport solver in the Virtual Environment for Reactor Analysis (VERA). As other parts of the code have been improved, the coarse mesh finite difference method (CMFD) has come to take up a significant portion of the runtime. Multilevel-in-energy CMFD and multilevel-in-space CMFD solvers have been used to improve CMFD solver performance. A new multilevel-in-space-and-energy CMFD solver is being introduced that combines components of these two methods. W-Cycles and partial W-Cycles are being investigated to further improve the efficiency of the multilevel-in-energy CMFD solver. The performance of these methods is demonstrated on full core reactor physics problems of interest to VERA.

Combining Conflict-Driven Clause Learning and Chronological Backtracking for Propositional Model Counting

In propositional model counting, also named #SAT, the search space needs to be explored exhaustively, in contrast to SAT, where the task is to determine whether a propositional formula is satisfiable. While state-of-the-art SAT solvers are based on non- chronological backtracking, it has also been shown that backtracking chronologically does not significantly degrade solver performance. Hence investigating the combination of chronological backtracking with conflict-driven clause learning (CDCL) for #SAT seems evident. We present a calculus for #SAT combining chronological backtracking with CDCL and provide a formal proof of its correctness.

Predicting SAT Solver Performance on Heterogeneous Hardware

In recent years, a lot of effort has been expended in determining if SAT solver performance is predictable. However, the work in this area invariably focuses on individual machines, and often on individual solvers. It is unclear whether predictions made on a specific solver and machine are accurate when translated to other solvers and hardware. In this work we consider five state-of-the-art solvers, 26 machines and 143 feature instances selected from the 2011 to 2014 SAT competitions. Using combinations of solvers, machines and instances we present four results: First, we show that UNSAT instances are more predictable than corresponding SAT instances. Second, we show that the number of cores in a machine has more impact on performance than L2 cache size. Third, we show that instances with fewer reused clauses are more CPU bound than those where clause reuse is high. Finally, we make accurate predictions of solution time for each of the instances considered across a diverse set of machines.

An Empirical Study of Branching Heuristics through the Lens of Global Learning Rate

In this paper, we analyze a suite of 7 well-known branching heuristics proposed by the SAT community and show that the better heuristics tend to generate more learnt clauses per decision, a metric we define as the global learning rate (GLR). We propose GLR as a metric for the branching heuristic to optimize. We test our hypothesis by developing a new branching heuristic that maximizes GLR greedily. We show empirically that this heuristic achieves very high GLR and interestingly very low literal block distance (LBD) over the learnt clauses. In our experiments this greedy branching heuristic enables the solver to solve instances faster than VSIDS, when the branching time is taken out of the equation. This experiment is a good proof of concept that a branching heuristic maximizing GLR will lead to good solver performance modulo the computational overhead. Finally, we propose a new branching heuristic, called SGDB, that uses machine learning to cheapily approximate greedy maximization of GLR. We show experimentally that SGDB performs on par with the VSIDS branching heuristic.

Improving SAT Solver Performance with Structure-based Preferential Bumping

We present a method we call structure-based preferential bumping,as a low-cost way to exploit formula structure in VSIDS-based SAT solvers.We show that the Glucose SAT solver, when modified with preferential bumpingof certain easily identified structurally important variables,out-performs unmodified Glucose on the industrial formulasfrom recent SAT solver competitions.