On the public citation graph
Scheduling Concurrent Applications on a Cluster of CPU-GPU Nodes
0 signed · 78 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.
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known work and none has been retracted — and get a dated CiteStamped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.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1006/jpdc.2002.1869openalex-snapshot
Using Moldability to Improve the Performance of Supercomputer Jobs
inferredcites →https://doi.org/10.1007/10968987_5openalex-snapshot
Scheduling of Parallel Jobs in a Heterogeneous Multi-site Environment
inferredcites →https://doi.org/10.1007/11407522_1openalex-snapshot
Parallel Job Scheduling — A Status Report
inferredcites →https://doi.org/10.1007/3-540-36265-7_17openalex-snapshot
Effective Selection of Partition Sizes for Moldable Scheduling of Parallel Jobs
inferredcites →https://doi.org/10.1007/978-3-540-89740-8_2openalex-snapshot
MCUDA: An Efficient Implementation of CUDA Kernels for Multi-core CPUs
inferredcites →https://doi.org/10.1007/s10766-005-3584-4openalex-snapshot
New Grid Scheduling and Rescheduling Methods in the GrADS Project
inferredcites →https://doi.org/10.1016/j.cpc.2010.12.052openalex-snapshot
Swan: A tool for porting CUDA programs to OpenCL
inferredcites →https://doi.org/10.1016/j.future.2009.07.003openalex-snapshot
Time and cost trade-off management for scheduling parallel applications on Utility Grids
inferredcites →https://doi.org/10.1049/ip-cdt:19982345openalex-snapshot
DPS: dynamic priority scheduling heuristic for heterogeneous computing systems
inferredcites →https://doi.org/10.1109/clustr.2009.5289193openalex-snapshot
Coordinating the use of GPU and CPU for improving performance of compute intensive applications
inferredcites →https://doi.org/10.1109/hcw.1998.666558openalex-snapshot
Scheduling resources in multi-user, heterogeneous, computing environments with SmartNet
inferredcites →https://doi.org/10.1109/hcw.1999.765094openalex-snapshot
Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems
inferredcites →https://doi.org/10.1109/hpdc.2005.1520949openalex-snapshot
Assessment and enhancement of meta-schedulers for multi-site job sharing
inferredcites →https://doi.org/10.1109/iiswc.2009.5306797openalex-snapshot
Rodinia: A benchmark suite for heterogeneous computing
inferredcites →https://doi.org/10.1109/iiswc.2009.5306801openalex-snapshot
A characterization and analysis of PTX kernels
inferredcites →https://doi.org/10.1109/ipdps.2004.1303222openalex-snapshot
New grid scheduling and rescheduling methods in the GrADS project
inferredcites →https://doi.org/10.1109/ipdps.2011.289openalex-snapshot
Comprehensive Performance Monitoring for GPU Cluster Systems
inferredcites →https://doi.org/10.1109/ipdps.2012.65openalex-snapshot
MATE-CG: A Map Reduce-Like Framework for Accelerating Data-Intensive Computations on Heterogeneous Clusters
inferredcites →https://doi.org/10.1109/isca.2004.1310764openalex-snapshot
Single-ISA heterogeneous multi-core architectures for multithreaded workload performance
inferredcites →https://doi.org/10.1109/sc.2004.49openalex-snapshot
Realistic Modeling and Svnthesis of Resources for Computational Grids
inferredcites →https://doi.org/10.1109/sc.2010.36openalex-snapshot
OpenMPC: Extended OpenMP Programming and Tuning for GPUs
inferredcites →https://doi.org/10.1145/1028176.1006707openalex-snapshot
Single-ISA Heterogeneous Multi-Core Architectures for Multithreaded Workload Performance
inferredcites →https://doi.org/10.1145/1128022.1128029openalex-snapshot
Dynamic thread assignment on heterogeneous multiprocessor architectures
inferredcites →https://doi.org/10.1145/1188455.1188464openalex-snapshot
TORQUE---TORQUE resource manager
inferredcites →https://doi.org/10.1145/1188455.1188559openalex-snapshot
Grid allocation and reservation---Improving grid resource allocation via integrated selection and binding
inferredcites →https://doi.org/10.1145/1365490.1365500openalex-snapshot
Scalable Parallel Programming with CUDA
inferredcites →https://doi.org/10.1145/1401132.1401152openalex-snapshot
Scalable parallel programming with CUDA
inferredcites →https://doi.org/10.1145/169627.169848openalex-snapshot
Performance analysis of job scheduling policies in parallel supercomputing environments
inferredcites →https://doi.org/10.1145/1735688.1735696openalex-snapshot
Modeling GPU-CPU workloads and systems
inferredcites →https://doi.org/10.1145/1810085.1810106openalex-snapshot
Compiler and runtime support for enabling generalized reduction computations on heterogeneous parallel configurations
inferredcites →https://doi.org/10.1145/1810479.1810498openalex-snapshot
Data-aware scheduling of legacy kernels on heterogeneous platforms with distributed memory
inferredcites →https://doi.org/10.1145/233008.233020openalex-snapshot
Effective distributed scheduling of parallel workloads
inferredcites →https://doi.org/10.1145/309746.309750openalex-snapshot
The elusive goal of workload characterization
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.5606openalex-snapshot
RALB‐HC: A resource‐aware load balancer for heterogeneous cluster
inferred← citeshttps://doi.org/10.1007/978-3-032-18765-9_7openalex-snapshot
Workload Placement and Scheduling on Heterogeneous CPU-GPU Architectures
inferred← citeshttps://doi.org/10.1007/978-981-95-6426-2_23openalex-snapshot
Quantification-Driven Coordinate Scheduling for Heterogeneous Resources
inferred← citeshttps://doi.org/10.1007/s00500-020-05152-8openalex-snapshot
A load balance multi-scheduling model for OpenCL kernel tasks in an integrated cluster
inferred← citeshttps://doi.org/10.1007/s00607-021-00958-2openalex-snapshot
FusionCL: a machine-learning based approach for OpenCL kernel fusion to increase system performance
inferred← citeshttps://doi.org/10.1007/s10766-016-0482-xopenalex-snapshot
Accelerating Data Analytics on Integrated GPU Platforms via Runtime Specialization
inferred← citeshttps://doi.org/10.1007/s11227-021-03883-5openalex-snapshot
Scheduling for heterogeneous systems in accelerator-rich environments
inferred← citeshttps://doi.org/10.1016/j.eswa.2017.11.002openalex-snapshot
Power consumption minimization by distributive particle swarm optimization for luminance control and its parallel implementations
inferred← citeshttps://doi.org/10.1016/j.future.2016.03.011openalex-snapshot
A user mode CPU–GPU scheduling framework for hybrid workloads
inferred← citeshttps://doi.org/10.1016/j.jpdc.2014.06.001openalex-snapshot
An execution time and energy model for an energy-aware execution of a conjugate gradient method with CPU/GPU collaboration
inferred← citeshttps://doi.org/10.1016/j.jpdc.2019.05.015openalex-snapshot
Troodon: A machine-learning based load-balancing application scheduler for CPU–GPU system
inferred← citeshttps://doi.org/10.1016/j.parco.2016.05.006openalex-snapshot
MultiCL: Enabling automatic scheduling for task-parallel workloads in OpenCL
inferred← citeshttps://doi.org/10.1016/j.procs.2013.05.193openalex-snapshot
Dynamic Distribution of Workload between CPU and GPU for a Parallel Conjugate Gradient Method in an Adaptive FEM
inferred← citeshttps://doi.org/10.1051/jnwpu/20213930529openalex-snapshot
A unified schedule policy of distributed machine learning framework for CPU-GPU cluster
inferred← citeshttps://doi.org/10.1109/cccs.2019.8888151openalex-snapshot
Optimizing on-demand GPUs in the Cloud for Deep Learning Applications Training
inferred← citeshttps://doi.org/10.1109/ccgrid.2013.19openalex-snapshot
Collocating CPU-only Jobs with GPU-assisted Jobs on GPU-assisted HPC
inferred← citeshttps://doi.org/10.1109/cluster.2013.6702679openalex-snapshot
Oncilla: A GAS runtime for efficient resource allocation and data movement in accelerated clusters
inferred← citeshttps://doi.org/10.1109/cluster.2015.15openalex-snapshot
Automatic Command Queue Scheduling for Task-Parallel Workloads in OpenCL
inferred← citeshttps://doi.org/10.1109/escience.2015.15openalex-snapshot
Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application
inferred← citeshttps://doi.org/10.1109/hipc.2014.7116892openalex-snapshot
A flexible scheduling framework for heterogeneous CPU-GPU clusters
inferred← citeshttps://doi.org/10.1109/hipc.2014.7116910openalex-snapshot
Smart multi-task scheduling for OpenCL programs on CPU/GPU heterogeneous platforms
inferred← citeshttps://doi.org/10.1109/hpcc.and.euc.2013.64openalex-snapshot
Consolidating Applications for Energy Efficiency in Heterogeneous Computing Systems
inferred← citeshttps://doi.org/10.1109/hpcsim.2014.6903679openalex-snapshot
Porting a neuro-imaging application to a CPU-GPU cluster
inferred← citeshttps://doi.org/10.1109/hpcsim.2016.7568444openalex-snapshot
schedGPU: Fine-grain dynamic and adaptative scheduling for GPUs
inferred← citeshttps://doi.org/10.1109/ipdpsw.2016.105openalex-snapshot
Controlling the Memory Subscription of Distributed Applications with a Task-Based Runtime System
inferred← citeshttps://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00028openalex-snapshot
Vapor: A GPU Sharing Scheduler with Communication and Computation Pipeline for Distributed Deep Learning
inferred← citeshttps://doi.org/10.1109/jstars.2015.2390626openalex-snapshot
Online Task Scheduling for LiDAR Data Preprocessing on Hybrid GPU/CPU Devices: A Reinforcement Learning Approach
inferred← citeshttps://doi.org/10.1109/mascots.2016.35openalex-snapshot
Diplomat: Mapping of Multi-kernel Applications Using a Static Dataflow Abstraction
inferred← citeshttps://doi.org/10.1109/pdcat.2014.26openalex-snapshot
Using rCUDA to Reduce GPU Resource-Assignment Fragmentation Caused by Job Scheduler
inferred← citeshttps://doi.org/10.1109/ross51935.2020.00011openalex-snapshot
Locality-Aware Scheduling for Scalable Heterogeneous Environments
inferred← citeshttps://doi.org/10.1109/sc.2012.111openalex-snapshot
ValuePack: Value-based scheduling framework for CPU-GPU clusters
inferred← citeshttps://doi.org/10.1109/tcc.2018.2874011openalex-snapshot
FPGA Resource Pooling in Cloud Computing
inferred← citeshttps://doi.org/10.1109/tpds.2017.2784428openalex-snapshot
Intra-Node Memory Safe GPU Co-Scheduling
inferred← citeshttps://doi.org/10.1145/2462902.2462911openalex-snapshot
A preemption-based runtime to efficiently schedule multi-process applications on heterogeneous clusters with GPUs
inferred← citeshttps://doi.org/10.1145/2517349openalex-snapshot
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
inferred← citeshttps://doi.org/10.1145/2903150.2903152openalex-snapshot
Accelerating graph applications on integrated GPU platforms via instrumentation-driven optimizations
inferred← citeshttps://doi.org/10.14778/3685800.3685845openalex-snapshot
Workload Placement on Heterogeneous CPU-GPU Systems
inferred← citeshttps://doi.org/10.17760/d20204341openalex-snapshot
Architectural and runtime enhancements for dynamically controlled multi-level concurrency on GPUs
inferred← citeshttps://doi.org/10.3390/en16020890openalex-snapshot
Energy-Aware Scheduling for High-Performance Computing Systems: A Survey
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.