On the public citation graph
Synchronization Trade-Offs in GPU Implementations of Graph Algorithms
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-540-77220-0_21openalex-snapshot
Accelerating Large Graph Algorithms on the GPU Using CUDA
inferredcites →https://doi.org/10.1007/978-3-642-19595-2_10openalex-snapshot
A Programming Language Interface to Describe Transformations and Code Generation
inferredcites →https://doi.org/10.1007/978-3-642-33347-7_17openalex-snapshot
Stochastic Gradient Descent with GPGPU
inferredcites →https://doi.org/10.1038/44565openalex-snapshot
Learning the parts of objects by non-negative matrix factorization
inferredcites →https://doi.org/10.1088/1742-6596/16/1/071openalex-snapshot
OSKI: A library of automatically tuned sparse matrix kernels
inferredcites →https://doi.org/10.1109/cluster.2011.34openalex-snapshot
Performance Characterization and Optimization of Atomic Operations on AMD GPUs
inferredcites →https://doi.org/10.1109/hpcc.2010.44openalex-snapshot
Exploiting Parallelism in Iterative Irregular Maxflow Computations on GPU Accelerators
inferredcites →https://doi.org/10.1109/icdar.2005.251openalex-snapshot
Using GPUs for machine learning algorithms
inferredcites →https://doi.org/10.1109/ipdps.2014.45openalex-snapshot
Work-Efficient Parallel GPU Methods for Single-Source Shortest Paths
inferredcites →https://doi.org/10.1109/ipdps.2014.59openalex-snapshot
Nitro: A Framework for Adaptive Code Variant Tuning
inferredcites →https://doi.org/10.1109/tkde.2005.99openalex-snapshot
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
inferredcites →https://doi.org/10.1137/120883153openalex-snapshot
Stencil-Aware GPU Optimization of Iterative Solvers
inferredcites →https://doi.org/10.1145/1390156.1390170openalex-snapshot
Fast support vector machine training and classification on graphics processors
inferredcites →https://doi.org/10.1145/1553374.1553486openalex-snapshot
Large-scale deep unsupervised learning using graphics processors
inferredcites →https://doi.org/10.1145/1572769.1572796openalex-snapshot
Fast minimum spanning tree for large graphs on the GPU
inferredcites →https://doi.org/10.1145/1654059.1654078openalex-snapshot
Implementing sparse matrix-vector multiplication on throughput-oriented processors
inferredcites →https://doi.org/10.1145/2145816.2145831openalex-snapshot
A GPU implementation of inclusion-based points-to analysis
inferredcites →https://doi.org/10.1145/2145816.2145842openalex-snapshot
Scalable parallel minimum spanning forest computation
inferredcites →https://doi.org/10.1145/2370036.2145831openalex-snapshot
A GPU implementation of inclusion-based points-to analysis
inferredcites →https://doi.org/10.1145/2602988.2602993openalex-snapshot
Massive atomics for massive parallelism on GPUs
inferredcites →https://doi.org/10.1145/2716282.2716289openalex-snapshot
Stochastic gradient descent on GPUs
inferredcites →https://doi.org/10.1145/2737924.2738003openalex-snapshot
Loop and data transformations for sparse matrix code
inferredcites →https://doi.org/10.48550/arxiv.1312.6186openalex-snapshot
GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-319-52709-3_17openalex-snapshot
Optimizing LOBPCG: Sparse Matrix Loop and Data Transformations in Action
inferred← citeshttps://doi.org/10.1109/ccgrid51090.2021.00038openalex-snapshot
Characterizing Input-sensitivity in Tightly-Coupled Collaborative Graph Algorithms
inferred← citeshttps://doi.org/10.1109/ia3.2016.011openalex-snapshot
Compiler Transformation to Generate Hybrid Sparse Computations
inferred← citeshttps://doi.org/10.1109/igsc48788.2019.8957183openalex-snapshot
Energy-Efficient GPU Graph Processing with On-Demand Page Migration
inferred← citeshttps://doi.org/10.1109/igsc55832.2022.9969364openalex-snapshot
Optimal Launch Bound Selection in CPU-GPU Hybrid Graph Applications with Deep Learning
inferred← citeshttps://doi.org/10.1109/ispass48437.2020.00027openalex-snapshot
Specializing Coherence, Consistency, and Push/Pull for GPU Graph Analytics
inferred← citeshttps://doi.org/10.1109/pact.2017.41openalex-snapshot
Graphie: Large-Scale Asynchronous Graph Traversals on Just a GPU
inferred← citeshttps://doi.org/10.1109/tpds.2022.3185212openalex-snapshot
Parallel Fractional Stochastic Gradient Descent With Adaptive Learning for Recommender Systems
inferred← citeshttps://doi.org/10.1145/3106237.3106265openalex-snapshot
Cooperative kernels: GPU multitasking for blocking algorithms
inferred← citeshttps://doi.org/10.1145/3293883.3295716openalex-snapshot
A pattern based algorithmic autotuner for graph processing on GPUs
inferred← citeshttps://doi.org/10.1145/3390523openalex-snapshot
A Conflict-free Scheduler for High-performance Graph Processing on Multi-pipeline FPGAs
inferred← citeshttps://doi.org/10.1145/3832810.3832923openalex-snapshot
DyGMIS: Fast and Scalable Fully Dynamic MIS Maintenance on Large Evolving Graphs
inferred← citeshttps://doi.org/10.14778/3151113.3151122openalex-snapshot
Accelerating dynamic graph analytics on GPUs
inferred← citeshttps://doi.org/10.26053/0h-n2q9-he00openalex-snapshot
An integrated compiler and runtime framework for sparse matrix codes
inferred← citeshttps://doi.org/10.5555/3018843.3018849openalex-snapshot
Compiler transformation to generate hybrid sparse computations
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