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Towards GPU-Accelerated Large-Scale Graph Processing in the Cloud
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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/10.1109/cit.2010.204openalex-snapshot
Efficiently Using a CUDA-enabled GPU as Shared Resource
inferredcites →https://doi.org/10.1109/cvprw.2008.4563095openalex-snapshot
CUDA cuts: Fast graph cuts on the GPU
inferredcites →https://doi.org/10.1109/fccm.2006.45openalex-snapshot
GraphStep: A System Architecture for Sparse-Graph Algorithms
inferredcites →https://doi.org/10.1109/hpcs.2010.5547126openalex-snapshot
rCUDA: Reducing the number of GPU-based accelerators in high performance clusters
inferredcites →https://doi.org/10.1109/icdm.2009.14openalex-snapshot
PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations
inferredcites →https://doi.org/10.1109/icpp.2011.88openalex-snapshot
GPU Resource Sharing and Virtualization on High Performance Computing Systems
inferredcites →https://doi.org/10.1109/ipdps.2007.370685openalex-snapshot
Software and Algorithms for Graph Queries on Multithreaded Architectures
inferredcites →https://doi.org/10.1109/ipdps.2008.4536261openalex-snapshot
SNAP, Small-world Network Analysis and Partitioning: An open-source parallel graph framework for the exploration of large-scale networks
inferredcites →https://doi.org/10.1109/ipdps.2013.37openalex-snapshot
On Graphs, GPUs, and Blind Dating: A Workload to Processor Matchmaking Quest
inferredcites →https://doi.org/10.1109/saahpc.2012.12openalex-snapshot
On Improving the Performance of Multi-threaded CUDA Applications with Concurrent Kernel Execution by Kernel Reordering
inferredcites →https://doi.org/10.1109/sc.2008.5214359openalex-snapshot
Benchmarking GPUs to tune dense linear algebra
inferredcites →https://doi.org/10.1109/tpds.2010.158openalex-snapshot
Mars: Accelerating MapReduce with Graphics Processors
inferredcites →https://doi.org/10.1109/tpds.2013.111openalex-snapshot
Medusa: Simplified Graph Processing on GPUs
inferredcites →https://doi.org/10.1109/tpds.2013.257openalex-snapshot
Kernelet: High-Throughput GPU Kernel Executions with Dynamic Slicing and Scheduling
inferredcites →https://doi.org/10.1137/1.9781611972740.43openalex-snapshot
R-MAT: A Recursive Model for Graph Mining
inferredcites →https://doi.org/10.1137/s1064827595287997openalex-snapshot
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
inferredcites →https://doi.org/10.1145/1376616.1376670openalex-snapshot
Relational joins on graphics processors
inferredcites →https://doi.org/10.1145/1830252.1830263openalex-snapshot
Design patterns for efficient graph algorithms in MapReduce
inferredcites →https://doi.org/10.1145/1835804.1835874openalex-snapshot
Parallel SimRank computation on large graphs with iterative aggregation
inferredcites →https://doi.org/10.1145/1837274.1837289openalex-snapshot
An effective GPU implementation of breadth-first search
inferredcites →https://doi.org/10.1145/1941553.1941590openalex-snapshot
Accelerating CUDA graph algorithms at maximum warp
inferredcites →https://doi.org/10.1145/1996130.1996160openalex-snapshot
Supporting GPU sharing in cloud environments with a transparent runtime consolidation framework
inferredcites →https://doi.org/10.1145/2063384.2063392openalex-snapshot
Optimizing symmetric dense matrix-vector multiplication on GPUs
inferredcites →https://doi.org/10.1145/2391229.2391232openalex-snapshot
Improving large graph processing on partitioned graphs in the cloud
inferredcites →https://doi.org/10.14778/2536274.2536293openalex-snapshot
Parallel graph processing on graphics processors made easy
inferredcites →https://doi.org/10.2312/eggh/eggh08/047-055openalex-snapshot
All-Pairs Shortest-Paths for Large Graphs on the GPU
inferredcites →https://doi.org/10.48550/arxiv.1006.4990openalex-snapshot
GraphLab: A New Framework for Parallel Machine Learning
inferredcites →https://doi.org/10.48550/arxiv.1204.6078openalex-snapshot
Distributed GraphLab: A Framework for Machine Learning in the Cloud
inferredcites →https://doi.org/10.48550/arxiv.1408.2041openalex-snapshot
GraphLab: A New Framework For Parallel Machine Learning
inferredcites →https://doi.org/10.5555/1413370.1413402openalex-snapshot
Benchmarking GPUs to tune dense linear algebra
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.4432openalex-snapshot
An efficient iterative graph data processing framework based on bulk synchronous parallel model
inferred← citeshttps://doi.org/10.1007/978-1-4939-1905-5_5openalex-snapshot
GPU-Accelerated Cloud Computing for Data-Intensive Applications
inferred← citeshttps://doi.org/10.1007/978-3-031-24628-9_22openalex-snapshot
Cloud Big Data Mining and Analytics: Bringing Greenness and Acceleration in the Cloud
inferred← citeshttps://doi.org/10.1007/978-3-319-30695-7_2openalex-snapshot
Design and Evaluation of a Processing-in-Memory Architecture for the Smart Memory Cube
inferred← citeshttps://doi.org/10.1007/978-981-13-1742-2_13openalex-snapshot
Accelerate the Execution of Graph Processing Using GPU
inferred← citeshttps://doi.org/10.1109/cloudcom.2014.120openalex-snapshot
Towards Economic Fairness for Big Data Processing in Pay-as-You-Go Cloud Computing
inferred← citeshttps://doi.org/10.1109/icdmw.2018.00144openalex-snapshot
Review of Graph Processing Frameworks
inferred← citeshttps://doi.org/10.1109/iiswc66894.2025.00030openalex-snapshot
ALPHA-PIM: Analysis of Linear Algebraic Processing for High-Performance Graph Applications on a Real Processing-In-Memory System
inferred← citeshttps://doi.org/10.1109/pervasive.2015.7087123openalex-snapshot
GPU accelerated video conversion in the cloud
inferred← citeshttps://doi.org/10.1145/3284557.3284563openalex-snapshot
A Study of Reconfigurable Accelerators for Cloud Computing
inferred← citeshttps://doi.org/10.32657/10356/59975openalex-snapshot
Parallel graph processing on graphics processing units
inferred← citeshttps://doi.org/10.3390/electronics13112011openalex-snapshot
Performance Evaluation of Parallel Graphs Algorithms Utilizing Graphcore IPU
inferred← citeshttps://doi.org/10.48550/arxiv.1802.01639openalex-snapshot
A Data as a Service (DaaS) Model for GPU-based Data Analytics
inferred← citeshttps://doi.org/10.48550/arxiv.2109.01661openalex-snapshot
Data science and Machine learning in the Clouds: A Perspective for the Future
inferred← citeshttps://doi.org/10.5821/dissertation-2117-344358openalex-snapshot
High-performance and energy-efficient irregular graph processing on GPU architectures
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