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Devito: Towards a Generic Finite Difference DSL Using Symbolic Python
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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High performance embedded architectures and compilers : fourth international conference, HiPEAC 2009, Paphos, Cyprus, January 25-28, 2009 : proceedings
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Deriving Efficient Data Movement from Decoupled Access/Execute Specifications
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Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book
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Python: An Ecosystem for Scientific Computing
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Python for Scientists and Engineers
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inferredcites →https://doi.org/10.1109/sc.companion.2012.134openalex-snapshot
PyOP2: A High-Level Framework for Performance-Portable Simulations on Unstructured Meshes
inferredcites →https://doi.org/10.1109/sc.companion.2012.136openalex-snapshot
Towards Domain-Specific Computing for Stencil Codes in HPC
inferredcites →https://doi.org/10.1109/wolfhpc.2014.7openalex-snapshot
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inferredcites →https://doi.org/10.1137/120873558openalex-snapshot
Automated Derivation of the Adjoint of High-Level Transient Finite Element Programs
inferredcites →https://doi.org/10.1145/2259016.2259037openalex-snapshot
Auto-generation and auto-tuning of 3D stencil codes on GPU clusters
inferredcites →https://doi.org/10.1145/2464996.2467268openalex-snapshot
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PDEL—a language for partial differential equations
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Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures
inferredcites →https://doi.org/10.7717/peerj-cs.103openalex-snapshot
SymPy: symbolic computing in Python
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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