On the public citation graph
Optimizing and Auto-Tuning Iterative Stencil Loops for GPUs with the In-Plane Method
0 signed · 38 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s00450-010-0111-7openalex-snapshot
A multi-GPU accelerated solver for the three-dimensional two-phase incompressible Navier-Stokes equations
inferredcites →https://doi.org/10.1007/s11227-007-0111-yopenalex-snapshot
The memory behavior of cache oblivious stencil computations
inferredcites →https://doi.org/10.1016/j.jcp.2008.08.023openalex-snapshot
Large calculation of the flow over a hypersonic vehicle using a GPU
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/hpcsim.2009.5192786openalex-snapshot
Fast seismic modeling and Reverse Time Migration on a GPU cluster
inferredcites →https://doi.org/10.1109/icpads.2011.94openalex-snapshot
PADS: A Pattern-Driven Stencil Compiler-Based Tool for Reuse of Optimizations on GPGPUs
inferredcites →https://doi.org/10.1109/icpp.2009.38openalex-snapshot
Accelerating Lattice Boltzmann Fluid Flow Simulations Using Graphics Processors
inferredcites →https://doi.org/10.1109/ipdps.2010.5470421openalex-snapshot
An auto-tuning framework for parallel multicore stencil computations
inferredcites →https://doi.org/10.1109/ipdps.2011.70openalex-snapshot
PATUS: A Code Generation and Autotuning Framework for Parallel Iterative Stencil Computations on Modern Microarchitectures
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/10.1145/1034774.1034777openalex-snapshot
Automatic tiling of iterative stencil loops
inferredcites →https://doi.org/10.1145/1178597.1178605openalex-snapshot
Implicit and explicit optimizations for stencil computations
inferredcites →https://doi.org/10.1145/1513895.1513905openalex-snapshot
3D finite difference computation on GPUs using CUDA
inferredcites →https://doi.org/10.1145/1542275.1542312openalex-snapshot
Tuned and wildly asynchronous stencil kernels for hybrid CPU/GPU systems
inferredcites →https://doi.org/10.1145/1542275.1542313openalex-snapshot
Performance modeling and automatic ghost zone optimization for iterative stencil loops on GPUs
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/10.1145/2304576.2304619openalex-snapshot
High-performance code generation for stencil computations on GPU architectures
inferredcites →https://doi.org/10.5555/1413370.1413375openalex-snapshot
Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cpe.4267openalex-snapshot
Exploiting GPU memory hierarchy for accelerating a specialized stencil computation
inferred← citeshttps://doi.org/10.1002/cpe.5635openalex-snapshot
Autotuning of configuration for program execution in GPUs
inferred← citeshttps://doi.org/10.1007/s11227-015-1575-9openalex-snapshot
Tuning framework for stencil computation in heterogeneous parallel platforms
inferred← citeshttps://doi.org/10.1016/j.jocs.2025.102778openalex-snapshot
Efficient utilization of streaming multiprocessors for the implementation of particle filter on graphics processing unit
inferred← citeshttps://doi.org/10.1109/concapan.2015.7428464openalex-snapshot
An adaptation of the MWP-CWP model for a GPU architecture applied to 3-D stencil kernels
inferred← citeshttps://doi.org/10.1109/icpp.2015.39openalex-snapshot
Automatic Performance Tuning of Stencil Computations on GPUs
inferred← citeshttps://doi.org/10.1109/ipdps.2014.82openalex-snapshot
Enabling and Scaling a Global Shallow-Water Atmospheric Model on Tianhe-2
inferred← citeshttps://doi.org/10.1109/ipdpsw.2018.00027openalex-snapshot
High-Performance High-Order Stencil Computation on FPGAs Using OpenCL
inferred← citeshttps://doi.org/10.1109/tcc.2017.2710311openalex-snapshot
Fault Tolerant Stencil Computation on Cloud-Based GPU Spot Instances
inferred← citeshttps://doi.org/10.1145/3295500.3356162openalex-snapshot
A versatile software systolic execution model for GPU memory-bound kernels
inferred← citeshttps://doi.org/10.1145/3368826.3377904openalex-snapshot
AN5D: automated stencil framework for high-degree temporal blocking on GPUs
inferred← citeshttps://doi.org/10.1145/3570638openalex-snapshot
Optimization Techniques for GPU Programming
inferred← citeshttps://doi.org/10.2514/6.2017-4508openalex-snapshot
Unsteady Navier-Stokes Computations on GPU Architectures
inferred← citeshttps://doi.org/10.48550/arxiv.1810.09773openalex-snapshot
High Performance Computing with FPGAs and OpenCL
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.