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Data-Driven Versus Topology-driven Irregular Computations on GPUs
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Survey propagation: An algorithm for satisfiability
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Introduction to algorithms
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Accelerating Large Graph Algorithms on the GPU Using CUDA
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CUDA Solutions for the SSSP Problem
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An Efficient CUDA Implementation of the Tree-Based Barnes Hut n-Body Algorithm
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A performance study of general-purpose applications on graphics processors using CUDA
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A hierarchical O(N log N) force-calculation algorithm
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Optimistic parallelism requires abstractions
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Optimistic parallelism requires abstractions
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Guaranteed-quality mesh generation for curved surfaces
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Accelerating CUDA graph algorithms at maximum warp
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Synthesizing concurrent schedulers for irregular algorithms
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On-the-fly elimination of dynamic irregularities for GPU computing
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The tao of parallelism in algorithms
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The tao of parallelism in algorithms
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A GPU implementation of inclusion-based points-to analysis
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Dynamically managed data for CPU-GPU architectures
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Scheduling multithreaded computations by work stealing
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Distributed discrete-event simulation
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Task Management for Irregular-Parallel Workloads on the GPU
inferredcites →https://doi.org/10.4135/9781412973380.n22openalex-snapshot
An Introduction to Data Mining
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Survey propagation: An algorithm for satisfiability
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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