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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.
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-642-33078-0_9openalex-snapshot
A Multi-GPU Programming Library for Real-Time Applications
inferredcites →https://doi.org/10.1007/978-3-662-48096-0_17openalex-snapshot
Priority Queues Are Not Good Concurrent Priority Schedulers
inferredcites →https://doi.org/10.1016/j.compfluid.2014.12.010openalex-snapshot
Scalable multi-relaxation-time lattice Boltzmann simulations on multi-GPU cluster
inferredcites →https://doi.org/10.1090/conm/588openalex-snapshot
Graph Partitioning and Graph Clustering
inferredcites →https://doi.org/10.1109/ipdps.2018.00012openalex-snapshot
Optimizing Parallel Graph Connectivity Computation via Subgraph Sampling
inferredcites →https://doi.org/10.1109/pact.2003.1237995openalex-snapshot
Proceedings 12th International Conference on Parallel Architectures and Compilation Techniques - PACT 2003
inferredcites →https://doi.org/10.1109/pact.2017.41openalex-snapshot
Graphie: Large-Scale Asynchronous Graph Traversals on Just a GPU
inferredcites →https://doi.org/10.1137/s1064827595287997openalex-snapshot
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
inferredcites →https://doi.org/10.1145/1941553.1941590openalex-snapshot
Accelerating CUDA graph algorithms at maximum warp
inferredcites →https://doi.org/10.1145/1993498.1993501openalex-snapshot
The tao of parallelism in algorithms
inferredcites →https://doi.org/10.1145/2049662.2049663openalex-snapshot
The university of Florida sparse matrix collection
inferredcites →https://doi.org/10.1145/2370816.2370866openalex-snapshot
A yoke of oxen and a thousand chickens for heavy lifting graph processing
inferredcites →https://doi.org/10.1145/2517349.2522739openalex-snapshot
A lightweight infrastructure for graph analytics
inferredcites →https://doi.org/10.1145/2983990.2984015openalex-snapshot
A compiler for throughput optimization of graph algorithms on GPUs
inferredcites →https://doi.org/10.1145/3192366.3192404openalex-snapshot
Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics
inferredcites →https://doi.org/10.1145/3749740.3749753openalex-snapshot
A Bridging Model for Parallel Computation
inferredcites →https://doi.org/10.1145/857076.857078openalex-snapshot
The many faces of publish/subscribe
inferredcites →https://doi.org/10.1145/93542openalex-snapshot
Proceedings of the ACM SIGPLAN 1990 conference on Programming language design and implementation - PLDI '90
inferredcites →https://doi.org/10.1186/s12859-015-0485-4openalex-snapshot
NMF-mGPU: non-negative matrix factorization on multi-GPU systems
inferredcites →https://doi.org/10.14778/3157794.3157799openalex-snapshot
A distributed multi-GPU system for fast graph processing
inferredcites →https://doi.org/10.5555/305219.305248openalex-snapshot
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
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.8000openalex-snapshot
Single‐ and multi‐GPU computing on NVIDIA‐ and AMD‐based server platforms for solidification modeling application
inferred← citeshttps://doi.org/10.1007/978-3-032-13127-0_3openalex-snapshot
GEMM Algorithm for Multi-GPU Platforms with Regular Uneven Data Transfer Links
inferred← citeshttps://doi.org/10.1007/s11042-023-16185-0openalex-snapshot
SpMV and BiCG-Stab sparse solver on Multi-GPUs for reservoir simulation
inferred← citeshttps://doi.org/10.1016/j.future.2026.108575openalex-snapshot
B-Graphless: Batch-based serverless graph processing for embodied AI backends
inferred← citeshttps://doi.org/10.1109/access.2024.3372990openalex-snapshot
MIMD Programs Execution Support on SIMD Machines: A Holistic Survey
inferred← citeshttps://doi.org/10.1109/dac63849.2025.11133241openalex-snapshot
GraphFI: An Efficient Fault Injection Framework for Graph Processing on GPGPUs
inferred← citeshttps://doi.org/10.1109/hpca56546.2023.10070949openalex-snapshot
FinePack: Transparently Improving the Efficiency of Fine-Grained Transfers in Multi-GPU Systems
inferred← citeshttps://doi.org/10.1109/tc.2023.3251860openalex-snapshot
Optimizing GPU-Based Graph Sampling and Random Walk for Efficiency and Scalability
inferred← citeshttps://doi.org/10.1109/tpds.2023.3267943openalex-snapshot
TurboMGNN: Improving Concurrent GNN Training Tasks on GPU With Fine-Grained Kernel Fusion
inferred← citeshttps://doi.org/10.1145/3466752.3480083openalex-snapshot
Improving Address Translation in Multi-GPUs via Sharing and Spilling aware TLB Design
inferred← citeshttps://doi.org/10.1145/3466752.3480088openalex-snapshot
GPS: A Global Publish-Subscribe Model for Multi-GPU Memory Management
inferred← citeshttps://doi.org/10.1145/3556976openalex-snapshot
DynamAP: Architectural Support for Dynamic Graph Traversal on the Automata Processor
inferred← citeshttps://doi.org/10.1145/3559009.3569649openalex-snapshot
Locality-Aware Optimizations for Improving Remote Memory Latency in Multi-GPU Systems
inferred← citeshttps://doi.org/10.1145/3643597openalex-snapshot
ABSS: An Adaptive Batch-Stream Scheduling Module for Dynamic Task Parallelism on Chiplet-based Multi-Chip Systems
inferred← citeshttps://doi.org/10.1145/3708035.3736010openalex-snapshot
Analyzing GPU Utilization in HPC Workloads: Insights from Large-Scale Systems
inferred← citeshttps://doi.org/10.2139/ssrn.4611137openalex-snapshot
Mimd Programs Execution Support on Simd Machines
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