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GBTL-CUDA: Graph Algorithms and Primitives for GPUs
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.jpdc.2013.05.007openalex-snapshot
Efficient breadth first search on multi-GPU systems
inferredcites →https://doi.org/10.1109/hipc.2012.6507474openalex-snapshot
Task-based parallel breadth-first search in heterogeneous environments
inferredcites →https://doi.org/10.1109/hpec.2013.6670338openalex-snapshot
Standards for graph algorithm primitives
inferredcites →https://doi.org/10.1109/iiswc.2012.6402918openalex-snapshot
A quantitative study of irregular programs on GPUs
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/ipdpsw.2015.19openalex-snapshot
Graphulo: Linear Algebra Graph Kernels for NoSQL Databases
inferredcites →https://doi.org/10.1137/1.9780898719918openalex-snapshot
Graph Algorithms in the Language of Linear Algebra
inferredcites →https://doi.org/10.1137/110848244openalex-snapshot
Parallel Sparse Matrix-Matrix Multiplication and Indexing: Implementation and Experiments
inferredcites →https://doi.org/10.1145/1941553.1941590openalex-snapshot
Accelerating CUDA graph algorithms at maximum warp
inferredcites →https://doi.org/10.1145/2049662.2049663openalex-snapshot
The university of Florida sparse matrix collection
inferredcites →https://doi.org/10.1145/2688500.2688538openalex-snapshot
Gunrock: a high-performance graph processing library on the GPU
inferredcites →https://doi.org/10.1145/2833179.2833189openalex-snapshot
Dynamic parallelism for simple and efficient GPU graph algorithms
inferredcites →https://doi.org/10.1177/1094342011403516openalex-snapshot
The Combinatorial BLAS: design, implementation, and applications
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-981-95-5747-9_5openalex-snapshot
Performance Analysis of Different Accelerators
inferred← citeshttps://doi.org/10.1109/atit50783.2020.9349345openalex-snapshot
Development of an All-Based Method Using Blockchain Technologies and Cuda Technologies
inferred← citeshttps://doi.org/10.1109/cloudsummit54781.2022.00010openalex-snapshot
Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe
inferred← citeshttps://doi.org/10.1109/hpec.2016.7761646openalex-snapshot
Mathematical foundations of the GraphBLAS
inferred← citeshttps://doi.org/10.1109/hpec.2017.8091043openalex-snapshot
Fast linear algebra-based triangle counting with KokkosKernels
inferred← citeshttps://doi.org/10.1109/hpec.2017.8091098openalex-snapshot
Enabling massive deep neural networks with the GraphBLAS
inferred← citeshttps://doi.org/10.1109/hpec67600.2025.11196667openalex-snapshot
GraphBLAS Mathematical Opportunities: Parallel Hypersparse, Matrix Based Graph Streaming, and Complex-Index Matrices
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/ipdps53621.2022.00056openalex-snapshot
Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU
inferred← citeshttps://doi.org/10.1109/ipdpsw.2017.117openalex-snapshot
Design of the GraphBLAS API for C
inferred← citeshttps://doi.org/10.1109/ipdpsw.2017.48openalex-snapshot
Breadth-First Search with A Multi-Core Computer
inferred← citeshttps://doi.org/10.1109/ipdpsw.2017.71openalex-snapshot
Constructing Adjacency Arrays from Incidence Arrays
inferred← citeshttps://doi.org/10.1109/ipdpsw.2018.00059openalex-snapshot
PyGB: GraphBLAS DSL in Python with Dynamic Compilation Into Efficient C++
inferred← citeshttps://doi.org/10.1109/ipdpsw.2019.00053openalex-snapshot
LAGraph: A Community Effort to Collect Graph Algorithms Built on Top of the GraphBLAS
inferred← citeshttps://doi.org/10.1109/ipdpsw50202.2020.00049openalex-snapshot
A Roadmap for the GraphBLAS C++ API
inferred← citeshttps://doi.org/10.1109/ipdpsw52791.2021.00047openalex-snapshot
Introduction to GraphBLAS 2.0
inferred← citeshttps://doi.org/10.1109/ipdpsw55747.2022.00050openalex-snapshot
High-Performance GraphBLAS Backend Prototype for NEC SX-Aurora TSUBASA
inferred← citeshttps://doi.org/10.1109/ipdpsw55747.2022.00053openalex-snapshot
GraphBLAS: C++ Iterators for Sparse Matrices
inferred← citeshttps://doi.org/10.1109/ipdpsw59300.2023.00043openalex-snapshot
C++ and Interoperability Between Libraries: The GraphBLAS C++ Specification
inferred← citeshttps://doi.org/10.1109/mascots.2017.15openalex-snapshot
Performance Evaluation of Priority Queues for Fine-Grained Parallel Tasks on GPUs
inferred← citeshttps://doi.org/10.1145/3466795openalex-snapshot
GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU
inferred← citeshttps://doi.org/10.1147/jrd.2018.2846958openalex-snapshot
IBM POWER9 and cognitive computing
inferred← citeshttps://doi.org/10.48550/arxiv.1903.04395openalex-snapshot
A GraphBLAS Approach for Subgraph Counting
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