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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/10.1023/b:ijpp.0000029272.69895.c1openalex-snapshot
High Performance RDMA-Based MPI Implementation over InfiniBand
inferredcites →https://doi.org/10.1088/1742-6596/396/4/042059openalex-snapshot
APEnet+: a 3D Torus network optimized for GPU-based HPC Systems
inferredcites →https://doi.org/10.1109/icpp.2013.17openalex-snapshot
Efficient Inter-node MPI Communication Using GPUDirect RDMA for InfiniBand Clusters with NVIDIA GPUs
inferredcites →https://doi.org/10.1109/ipdps.2011.102openalex-snapshot
Multi-GPU MapReduce on GPU Clusters
inferredcites →https://doi.org/10.1109/tpami.2006.244openalex-snapshot
Face Description with Local Binary Patterns: Application to Face Recognition
inferredcites →https://doi.org/10.1145/1879141.1879175openalex-snapshot
Network traffic characteristics of data centers in the wild
inferredcites →https://doi.org/10.1145/2391229.2391237openalex-snapshot
Using vector interfaces to deliver millions of IOPS from a networked key-value storage server
inferredcites →https://doi.org/10.1145/2502524.2502548openalex-snapshot
Zero-copy I/O processing for low-latency GPU computing
inferredcites →https://doi.org/10.1145/2535372.2535412openalex-snapshot
On limitations of network acceleration
inferredcites →https://doi.org/10.1145/782814.782855openalex-snapshot
High performance RDMA-based MPI implementation over InfiniBand
inferredcites →https://doi.org/10.21276/ijre.2018.5.5.4openalex-snapshot
MapReduce: Simplified Data Processing on Large Cluster
inferredcites →https://doi.org/10.48550/arxiv.1207.4676openalex-snapshot
Proceedings of the 29th International Conference on Machine Learning (ICML-12)
inferredcites →https://doi.org/10.5555/1972457.1972459openalex-snapshot
SSLShader: cheap SSL acceleration with commodity processors
inferredcites →https://doi.org/10.5555/2387880.2387894openalex-snapshot
MegaPipe: a new programming interface for scalable network I/O
inferredcites →https://doi.org/10.5555/2523721.2523758openalex-snapshot
RSVM: a region-based software virtual memory for GPU
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/10.1007/978-3-030-20787-8_3openalex-snapshot
Energy Consumption Analysis and Time Estimation Model in GPU Cluster and MultiGPU in a High Computational Demand Problem
inferred← citeshttps://doi.org/10.1007/978-3-031-01759-9openalex-snapshot
General-Purpose Graphics Processor Architectures
inferred← citeshttps://doi.org/10.1007/978-3-032-18268-5_17openalex-snapshot
xPUBench: Scalable and Energy-Efficient GPU and DPU-Accelerated Network Functions
inferred← citeshttps://doi.org/10.1016/j.comcom.2022.03.021openalex-snapshot
Intelligent deep fusion network for urban traffic flow anomaly identification
inferred← citeshttps://doi.org/10.1109/aimlsystems67835.2025.11330244openalex-snapshot
Bypassing the CPU: GPU-Centric Video Inference for Edge Offloaded Applications
inferred← citeshttps://doi.org/10.1109/bigdata66926.2025.11402370openalex-snapshot
DEPUTY: A DPU-Based Network Offloading Architecture with Minimal CPU Involvement for Stable Network Performance
inferred← citeshttps://doi.org/10.1109/icitiit54346.2022.9744209openalex-snapshot
Parallelizing CPU-GPU Network Processing Flows
inferred← citeshttps://doi.org/10.1109/infocom42981.2021.9488699openalex-snapshot
GPU-Ether: GPU-native Packet I/O for GPU Applications on Commodity Ethernet
inferred← citeshttps://doi.org/10.1109/mcsoc51149.2021.00012openalex-snapshot
Host Bypassing: Direct Data Piping from the Network to the Hardware Accelerator
inferred← citeshttps://doi.org/10.1109/netsoft.2019.8806698openalex-snapshot
NetML: An NFV Platform with Efficient Support for Machine Learning Applications
inferred← citeshttps://doi.org/10.1109/noms54207.2022.9789822openalex-snapshot
Unleashing GPUs for Network Function Virtualization: an open architecture based on Vulkan and Kubernetes
inferred← citeshttps://doi.org/10.1109/pact.2019.00027openalex-snapshot
Achieving Scalability in a k-NN Multi-GPU Network Service with Centaur
inferred← citeshttps://doi.org/10.1109/pmbs51919.2020.00016openalex-snapshot
Performance Trade-offs in GPU Communication: A Study of Host and Device-initiated Approaches
inferred← citeshttps://doi.org/10.1109/tc.2020.2968302openalex-snapshot
XeFlow: Streamlining Inter-Processor Pipeline Execution for the Discrete CPU-GPU Platform
inferred← citeshttps://doi.org/10.1109/tc.2021.3084407openalex-snapshot
AEML: An Acceleration Engine for Multi-GPU Load-balancing in Distributed Heterogeneous Environment
inferred← citeshttps://doi.org/10.1109/tmm.2023.3274303openalex-snapshot
CARE: Cloudified Android With Optimized Rendering Platform
inferred← citeshttps://doi.org/10.1109/tpds.2021.3066407openalex-snapshot
Fine-Grained Multi-Query Stream Processing on Integrated Architectures
inferred← citeshttps://doi.org/10.1109/tpds.2022.3193368openalex-snapshot
Gaviss : Boosting the Performance of GPU-Accelerated NFV Systems via Data Sharing
inferred← citeshttps://doi.org/10.1109/ur49135.2020.9144839openalex-snapshot
Study on weld seam geometry control for connected gas metal arc welding systems
inferred← citeshttps://doi.org/10.1145/3273982.3273992openalex-snapshot
Algorithm/Architecture Co-Design for Near-Memory Processing
inferred← citeshttps://doi.org/10.1145/3315569openalex-snapshot
Augmenting Operating Systems with OpenCL Accelerators
inferred← citeshttps://doi.org/10.1145/3445814.3446734openalex-snapshot
Judging a type by its pointer: optimizing GPU virtual functions
inferred← citeshttps://doi.org/10.1145/3577193.3593713openalex-snapshot
Multi-GPU Communication Schemes for Iterative Solvers: When CPUs are Not in Charge
inferred← citeshttps://doi.org/10.1145/3589974openalex-snapshot
SplitRPC: A {Control + Data} Path Splitting RPC Stack for ML Inference Serving
inferred← citeshttps://doi.org/10.1145/3624062.3624173openalex-snapshot
GPU Graph Processing on CXL-Based Microsecond-Latency External Memory
inferred← citeshttps://doi.org/10.1145/3642961openalex-snapshot
Proceedings of the 3rd International Workshop on Extreme Heterogeneity Solutions
inferred← citeshttps://doi.org/10.1145/3642961.3643799openalex-snapshot
GPU-Initiated Resource Allocation for Irregular Workloads
inferred← citeshttps://doi.org/10.1145/3642968.3654820openalex-snapshot
Toward GPU-centric Networking on Commodity Hardware
inferred← citeshttps://doi.org/10.1145/3721145.3725748openalex-snapshot
DREAM: Device-Driven Efficient Access to Virtual Memory
inferred← citeshttps://doi.org/10.1145/3725783.3764406openalex-snapshot
A System-level Abstraction and Service for Flourishing AI-powered Applications
inferred← citeshttps://doi.org/10.2200/s00848ed1v01y201804cac044openalex-snapshot
General-Purpose Graphics Processor Architectures
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