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On-GPU Thread-Data Remapping for Branch Divergence Reduction
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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/spe.4380250705openalex-snapshot
ANTLR: A predicated‐ LL ( k ) parser generator
inferredcites →https://doi.org/10.1007/978-3-662-45761-0_1openalex-snapshot
GPU-Accelerated Database Systems: Survey and Open Challenges
inferredcites →https://doi.org/10.1007/s11265-015-1051-zopenalex-snapshot
OpenDwarfs: Characterization of Dwarf-Based Benchmarks on Fixed and Reconfigurable Architectures
inferredcites →https://doi.org/10.1109/hpca.2011.5749714openalex-snapshot
Thread block compaction for efficient SIMT control flow
inferredcites →https://doi.org/10.1109/iiswc.2009.5306797openalex-snapshot
Rodinia: A benchmark suite for heterogeneous computing
inferredcites →https://doi.org/10.1109/iiswc.2011.6114174openalex-snapshot
Performance characterization of the NAS Parallel Benchmarks in OpenCL
inferredcites →https://doi.org/10.1109/ipdps.2012.18openalex-snapshot
An Accurate GPU Performance Model for Effective Control Flow Divergence Optimization
inferredcites →https://doi.org/10.1109/mcse.2010.69openalex-snapshot
OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems
inferredcites →https://doi.org/10.1109/micro.2007.12openalex-snapshot
Microarchitectural Design Space Exploration Using an Architecture-Centric Approach
inferredcites →https://doi.org/10.1109/pact.2011.63openalex-snapshot
Divergence Analysis and Optimizations
inferredcites →https://doi.org/10.1109/tcad.2015.2501303openalex-snapshot
An Accurate GPU Performance Model for Effective Control Flow Divergence Optimization
inferredcites →https://doi.org/10.1145/1810085.1810104openalex-snapshot
Streamlining GPU applications on the fly
inferredcites →https://doi.org/10.1145/1815961.1815992openalex-snapshot
Dynamic warp subdivision for integrated branch and memory divergence tolerance
inferredcites →https://doi.org/10.1145/1816038.1815992openalex-snapshot
Dynamic warp subdivision for integrated branch and memory divergence tolerance
inferredcites →https://doi.org/10.1145/1950365.1950408openalex-snapshot
On-the-fly elimination of dynamic irregularities for GPU computing
inferredcites →https://doi.org/10.1145/1961295.1950408openalex-snapshot
On-the-fly elimination of dynamic irregularities for GPU computing
inferredcites →https://doi.org/10.1145/1964179.1964184openalex-snapshot
Reducing branch divergence in GPU programs
inferredcites →https://doi.org/10.1145/2485922.2485954openalex-snapshot
SIMD divergence optimization through intra-warp compaction
inferredcites →https://doi.org/10.1145/2508148.2485954openalex-snapshot
SIMD divergence optimization through intra-warp compaction
inferredcites →https://doi.org/10.1145/2598394.2605689openalex-snapshot
Working with OpenCL to speed up a genetic programming financial forecasting algorithm
inferredcites →https://doi.org/10.1177/1094342015622672openalex-snapshot
A novel approach to train random forests on GPU for computer vision applications using local features
inferredcites →https://doi.org/10.5555/2537857.2537861openalex-snapshot
Fast and flexible: parallel packet processing with GPUs and click
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-69984-0_46openalex-snapshot
Contrived and Remediated GPU Thread Divergence Using a Flattening Technique
inferred← citeshttps://doi.org/10.1007/978-3-031-85638-9_7openalex-snapshot
Is Manual Code Optimization Still Required to Mitigate GPU Thread Divergence? Applying a Flattening Technique to Observe Performance
inferred← citeshttps://doi.org/10.1016/j.jpdc.2019.06.009openalex-snapshot
Efficient low-latency packet processing using On-GPU Thread-Data Remapping
inferred← citeshttps://doi.org/10.1016/j.jpdc.2020.02.003openalex-snapshot
On-GPU thread-data remapping for nested branch divergence
inferred← citeshttps://doi.org/10.1109/access.2019.2927261openalex-snapshot
A Parallel-Computing Algorithm for High-Energy Physics Particle Tracking and Decoding Using GPU Architectures
inferred← citeshttps://doi.org/10.1109/iaecst60924.2023.10503564openalex-snapshot
Development and Verification of the GPU-Based Monte Carlo Particle Transport Program MagiC
inferred← citeshttps://doi.org/10.1109/informatics62280.2024.10900832openalex-snapshot
Mitigating Execution Divergence in GPGPU Computing through Dynamic Data Remapping
inferred← citeshttps://doi.org/10.1109/isvlsi49217.2020.00049openalex-snapshot
Associative Thread Compaction for Efficient Control Flow Handling in GPGPUs
inferred← citeshttps://doi.org/10.1109/msn53354.2021.00073openalex-snapshot
Rosella: A Self-Driving Distributed Scheduler for Heterogeneous Clusters
inferred← citeshttps://doi.org/10.1145/3570638openalex-snapshot
Optimization Techniques for GPU Programming
inferred← citeshttps://doi.org/10.1145/3626957openalex-snapshot
Divergence Reduction in Monte Carlo Neutron Transport with On-GPU Asynchronous Scheduling
inferred← citeshttps://doi.org/10.1145/3649329.3658462openalex-snapshot
Control Flow Divergence Optimization by Exploiting Tensor Cores
inferred← citeshttps://doi.org/10.1145/3736170openalex-snapshot
AOBO: A Fast-Switching Online Binary Optimizer on AArch64
inferred← citeshttps://doi.org/10.1145/3769118openalex-snapshot
Optimal Split Point Placement for Predictable GPU Wavefront Splitting
inferred← citeshttps://doi.org/10.2197/ipsjjip.30.164openalex-snapshot
Scalar Replacement Considering Branch Divergence
inferred← citeshttps://doi.org/10.3390/pr8091199openalex-snapshot
A Representation of Membrane Computing with a Clustering Algorithm on the Graphical Processing Unit
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