On the public citation graph

RIFLING: A reinforcement learning‐based GPU scheduler for deep learning research and development platforms

https://doi.org/10.1002/spe.3066
0 signed · 40 inferred · view as graph

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.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-88224-2_7openalex-snapshot
A HPC Co-scheduler with Reinforcement Learning
inferredcites →https://doi.org/10.1007/s10766-021-00711-4openalex-snapshot
M-DRL: Deep Reinforcement Learning Based Coflow Traffic Scheduler with MLFQ Threshold Adaption
inferredcites →https://doi.org/10.1016/0921-8890(95)00026-copenalex-snapshot
Learning from delayed rewards
inferredcites →https://doi.org/10.1016/j.neucom.2015.08.066openalex-snapshot
PR-ELM: Parallel regularized extreme learning machine based on cluster
inferredcites →https://doi.org/10.1016/j.neucom.2016.05.112openalex-snapshot
Heterogeneous blocked CPU-GPU accelerate scheme for large scale extreme learning machine
inferredcites →https://doi.org/10.1016/s0921-8890(00)00087-7openalex-snapshot
Dynamic job-shop scheduling using reinforcement learning agents
inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1109/cvpr.2015.7299173openalex-snapshot
Convolutional neural networks at constrained time cost
inferredcites →https://doi.org/10.1109/infocom41043.2020.9155446openalex-snapshot
Preemptive All-reduce Scheduling for Expediting Distributed DNN Training
inferredcites →https://doi.org/10.1109/infocomwkshps50562.2020.9162977openalex-snapshot
Deep Reinforcement Learning for Controller Placement in Software Defined Network
inferredcites →https://doi.org/10.1109/iwqos.2018.8624175openalex-snapshot
Reducing Web Latency Through Dynamically Setting TCP Initial Window with Reinforcement Learning
inferredcites →https://doi.org/10.1109/jsac.2021.3078498openalex-snapshot
Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation
inferredcites →https://doi.org/10.1109/sc.2016.25openalex-snapshot
SERF: Efficient Scheduling for Fast Deep Neural Network Serving via Judicious Parallelism
inferredcites →https://doi.org/10.1109/sc41405.2020.00035openalex-snapshot
RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement Learning
inferredcites →https://doi.org/10.1109/tcc.2017.2773078openalex-snapshot
A Reinforcement Learning-Based Mixed Job Scheduler Scheme for Grid or IaaS Cloud
inferredcites →https://doi.org/10.1109/tpds.2019.2931558openalex-snapshot
Deep Learning Research and Development Platform: Characterizing and Scheduling with QoS Guarantees on GPU Clusters
inferredcites →https://doi.org/10.1145/2749469.2749472openalex-snapshot
DjiNN and Tonic
inferredcites →https://doi.org/10.1145/2996864openalex-snapshot
DianNao family
inferredcites →https://doi.org/10.1145/3005745.3005750openalex-snapshot
Resource Management with Deep Reinforcement Learning
inferredcites →https://doi.org/10.1145/3190508.3190517openalex-snapshot
Optimus
inferredcites →https://doi.org/10.1145/3341302.3342080openalex-snapshot
Learning scheduling algorithms for data processing clusters
inferredcites →https://doi.org/10.15224/978-1-63248-169-6-09openalex-snapshot
Multiple Resource Management and Burst Time Prediction using Deep Reinforcement Learning
inferredcites →https://doi.org/10.1561/2200000050openalex-snapshot
Convex Optimization: Algorithms and Complexity
inferredcites →https://doi.org/10.23919/date.2019.8715279openalex-snapshot
GENIE: QoS-guided Dynamic Scheduling for CNN-based Tasks on SME Clusters

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/spe.3159openalex-snapshot
DeepPlayer: An open‐source SignalPlant plugin for deep learning inference
inferred← citeshttps://doi.org/10.1002/spe.3284openalex-snapshot
DRS: A deep reinforcement learning enhanced Kubernetes scheduler for microservice‐based system
inferred← citeshttps://doi.org/10.1002/spe.70073openalex-snapshot
ML ‐Driven DevOps : An Empirical Framework for Predictive Optimization and Intelligent Automation in CI / CD Pipelines
inferred← citeshttps://doi.org/10.1007/978-981-96-5032-3_2openalex-snapshot
DNN-Schedule: A Predictive Scheduler for Minimizing Interference of Co-located DNN Workload
inferred← citeshttps://doi.org/10.1016/j.future.2026.108612openalex-snapshot
Optimizing CPU scheduling and inter-VM communication for lightweight virtualization using reinforcement learning
inferred← citeshttps://doi.org/10.1016/j.jpdc.2025.105138openalex-snapshot
Topology-aware GPU job scheduling with deep reinforcement learning and heuristics
inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128030openalex-snapshot
ESEN: Efficient GPU sharing of Ensemble Neural Networks
inferred← citeshttps://doi.org/10.1016/j.neunet.2025.107703openalex-snapshot
ESCAN: Efficient GPU sharing for cascade neural network inference
inferred← citeshttps://doi.org/10.1109/iccd56317.2022.00103openalex-snapshot
Tear Up the Bubble Boom: Lessons Learned From a Deep Learning Research and Development Cluster
inferred← citeshttps://doi.org/10.1109/icde65448.2025.00021openalex-snapshot
BQSched: A Non-Intrusive Scheduler for Batch Concurrent Queries via Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/tcc.2025.3624031openalex-snapshot
DRKC: Deep Reinforcement Learning Enhanced Microservice Scheduling on Kubernetes Clusters in Cloud-Edge Environment
inferred← citeshttps://doi.org/10.1109/trustcom63139.2024.00232openalex-snapshot
Achieving Trusted GPU Allocation: An Empirical Study on Efficiency Changes of Deep Learning Training Tasks
inferred← citeshttps://doi.org/10.1145/3580305.3599241openalex-snapshot
A Dual-Agent Scheduler for Distributed Deep Learning Jobs on Public Cloud via Reinforcement Learning
inferred← citeshttps://doi.org/10.1145/3638757openalex-snapshot
Deep Learning Workload Scheduling in GPU Datacenters: A Survey
inferred← citeshttps://doi.org/10.1145/3772052.3772242openalex-snapshot
Defragmentation Scheduling with Deep Reinforcement Learning in Shared GPU Clusters
inferred← citeshttps://doi.org/10.3390/math10244788openalex-snapshot
Adaptive Distributed Parallel Training Method for a Deep Learning Model Based on Dynamic Critical Paths of DAG
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.