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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
SWIFT: Maintaining weak-scalability with a dynamic range ...
Josh Borrow, Richard G. Bower, Peter W. Draper, Pedro Gonnet, Ma · 2018-07-04 · via cs.DS updates on arXiv.org

Cosmological simulations require the use of a multiple time-stepping scheme. Without such a scheme, cosmological simulations would be impossible due to their high level of dynamic range; over eleven orders of magnitude in density. Such a large dynamic range leads to a range of over four orders of magnitude in time-step, which presents a significant load-balancing challenge. In this work, the extreme adaptivity that cosmological simulations present is tackled in three main ways through the use of the code SWIFT. First, an adaptive mesh is used to ensure that only the relevant particles are interacted in a given time-step. Second, task-based parallelism is used to ensure efficient load-balancing within a single node, using pthreads and SIMD vectorisation. Finally, a domain decomposition strategy is presented, using the graph domain decomposition library METIS, that bisects the work that must be performed by the simulation between nodes using MPI. These three strategies are shown to give SWIFT near-perfect weak-scaling characteristics, only losing 25% performance when scaling from 1 to 4096 cores on a representative problem, whilst being more than 30x faster than the de-facto standard Gadget-2 code.