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Optimization of geometric multigrid for emerging multi- and manycore processors
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/nla.1808openalex-snapshot
Improving the arithmetic intensity of multigrid with the help of polynomial smoothers
inferredcites →https://doi.org/10.1109/12.40842openalex-snapshot
Evaluating associativity in CPU caches
inferredcites →https://doi.org/10.1109/compsac.2009.82openalex-snapshot
Efficient Temporal Blocking for Stencil Computations by Multicore-Aware Wavefront Parallelization
inferredcites →https://doi.org/10.1109/ipdps.2000.845979openalex-snapshot
Using time skewing to eliminate idle time due to memory bandwidth and network limitations
inferredcites →https://doi.org/10.1109/ipdps.2008.4536295openalex-snapshot
Lattice Boltzmann simulation optimization on leading multicore platforms
inferredcites →https://doi.org/10.1109/sc.2000.10015openalex-snapshot
Tiling Optimizations for 3D Scientific Computations
inferredcites →https://doi.org/10.1109/sc.2008.5222004openalex-snapshot
Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures
inferredcites →https://doi.org/10.1109/sc.2010.2openalex-snapshot
3.5-D Blocking Optimization for Stencil Computations on Modern CPUs and GPUs
inferredcites →https://doi.org/10.1137/070693199openalex-snapshot
Optimization and Performance Modeling of Stencil Computations on Modern Microprocessors
inferredcites →https://doi.org/10.1137/110838844openalex-snapshot
Exposing Fine-Grained Parallelism in Algebraic Multigrid Methods
inferredcites →https://doi.org/10.1145/1048935.1050213openalex-snapshot
Evaluation of Cache-based Superscalar and Cacheless Vector Architectures for Scientific Computations
inferredcites →https://doi.org/10.1145/1128022.1128027openalex-snapshot
The potential of the cell processor for scientific computing
inferredcites →https://doi.org/10.1145/1654059.1654065openalex-snapshot
Autotuning multigrid with PetaBricks
inferredcites →https://doi.org/10.1145/1735688.1735702openalex-snapshot
The Scalable Heterogeneous Computing (SHOC) benchmark suite
inferredcites →https://doi.org/10.1145/2063384.2063458openalex-snapshot
Extracting ultra-scale Lattice Boltzmann performance via hierarchical and distributed auto-tuning
inferredcites →https://doi.org/10.1145/301618.301668openalex-snapshot
New tiling techniques to improve cache temporal locality
inferredcites →https://doi.org/10.1145/882262.882364openalex-snapshot
Sparse matrix solvers on the GPU
inferredcites →https://doi.org/10.1177/1094342004041295openalex-snapshot
Cache-Efficient Multigrid Algorithms
inferredcites →https://doi.org/10.1504/pcfd.2008.018088openalex-snapshot
Introducing a parallel cache oblivious blocking approach for the lattice Boltzmann method
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A quasi‐cache‐aware model for optimal domain partitioning in parallel geometric multigrid
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BOPS, A New Computation-Centric Metric for Datacenter Computing
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Optimizing and Scaling HPCG on Tianhe-2: Early Experience
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Performance Characterization and Optimization for Intel Xeon Phi Coprocessor
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Performance Evaluation of HPGMG on Tianhe-2: Early Experience
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Vectorized Higher Order Finite Difference Kernels
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Assessing the Performance of OpenMP Programs on the Intel Xeon Phi
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Numerical solution and convergence analysis of steam injection in heavy oil reservoirs
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Panda: A Compiler Framework for Concurrent CPU $$+$$ + GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers
inferred← citeshttps://doi.org/10.1007/s11227-014-1245-3openalex-snapshot
Microarchitectural performance comparison of Intel Knights Corner and Intel Sandy Bridge with CFD applications
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Application-specific feature selection and clustering approach with HPC system profiling data
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Automatic translation of MPI source into a latency-tolerant, data-driven form
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A Comprehensive Review of Machine Learning in Multi-objective Optimization
inferred← citeshttps://doi.org/10.1109/hpec.2015.7322480openalex-snapshot
GPU accelerated geometric multigrid method: Comparison with preconditioned conjugate gradient
inferred← citeshttps://doi.org/10.1109/icstcc.2015.7321289openalex-snapshot
GPU accelerated geometric multigrid method: Performance comparison on recent NVIDIA architectures
inferred← citeshttps://doi.org/10.1109/ipdps.2014.119openalex-snapshot
s-Step Krylov Subspace Methods as Bottom Solvers for Geometric Multigrid
inferred← citeshttps://doi.org/10.1109/ipdps.2015.103openalex-snapshot
Compiler-Directed Transformation for Higher-Order Stencils
inferred← citeshttps://doi.org/10.1109/ipdps.2015.88openalex-snapshot
Efficient and Simplified Parallel Graph Processing over CPU and MIC
inferred← citeshttps://doi.org/10.1109/ipdps.2017.98openalex-snapshot
Application Level Reordering of Remote Direct Memory Access Operations
inferred← citeshttps://doi.org/10.1109/ipdps.2018.00036openalex-snapshot
Improving Network Throughput with Global Communication Reordering
inferred← citeshttps://doi.org/10.1109/ipdpsw.2014.162openalex-snapshot
The Power-Performance Tradeoffs of the Intel Xeon Phi on HPC Applications
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Automating Compiler-Directed Autotuning for Phased Performance Behavior
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Communication Avoiding 2D Stencil Implementations over PaRSEC Task-Based Runtime
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Accelerating HPCG on Tianhe-2: A hybrid CPU-MIC algorithm
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Efficient Shared-Memory Implementation of High-Performance Conjugate Gradient Benchmark and its Application to Unstructured Matrices
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DBSR: An Efficient Storage Format for Vectorizing Sparse Triangular Solvers on Structured Grids
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High-Performance, Scalable Geometric Multigrid via Fine-Grain Data Blocking for GPUs
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Optimizing Multi-Grid Preconditioned Conjugate Gradient Method on Multi-Cores
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Segmental Refinement: A Multigrid Technique for Data Locality
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Optimizing geometric multigrid method computation using a DSL approach
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Temporal vectorization for stencils
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Enabling and scaling the HPCG benchmark on the newest generation Sunway supercomputer with 42 million heterogeneous cores
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BrickDL: Graph-Level Optimizations for DNNs with Fine-Grained Data Blocking on GPUs
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Towards making autotuning mainstream
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Towards simulation of subcellular calcium dynamics at nanometre resolution
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Optimizations in a high-performance conjugate gradient benchmark for IA-based multi- and many-core processors
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623 Tflop/s HPCG run on Tianhe-2: Leveraging millions of hybrid cores
inferred← citeshttps://doi.org/10.1177/1094342016672720openalex-snapshot
Reaching bandwidth saturation using transparent injection parallelization
inferred← citeshttps://doi.org/10.1177/10943420241268288openalex-snapshot
Bricks: A high-performance portability layer for computations on block-structured grids
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High-performance conjugate gradient benchmark: A comprehensive survey
inferred← citeshttps://doi.org/10.17771/pucrio.acad.28707openalex-snapshot
AUXÍLIO A PORTABILIDADE DE CÓDIGO EM APLICAÇÕES DE ALTO DESEMPENHO
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Performance Evaluation of Massively Parallel Systems Using SPEC OMP Suite
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