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Topology-Aware Data Aggregation for Intensive I/O on Large-Scale Supercomputers

https://doi.org/10.1109/comhpc.2016.013
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What this work says

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

inferredcites →https://doi.org/10.1007/3-540-45825-5_5openalex-snapshot
MPICH2: A New Start for MPI Implementations
inferredcites →https://doi.org/10.1007/978-3-540-92859-1_14openalex-snapshot
Data Locality Aware Strategy for Two-Phase Collective I/O
inferredcites →https://doi.org/10.1007/978-3-642-01970-8_19openalex-snapshot
Performance Evaluation of Collective Write Algorithms in MPI I/O
inferredcites →https://doi.org/10.1016/s0167-8191(01)00129-6openalex-snapshot
Optimizing noncontiguous accesses in MPI–IO
inferredcites →https://doi.org/10.1109/cluster.2011.79openalex-snapshot
Automatically Selecting the Number of Aggregators for Collective I/O Operations
inferredcites →https://doi.org/10.1109/cluster.2015.29openalex-snapshot
Collective I/O Tuning Using Analytical and Machine Learning Models
inferredcites →https://doi.org/10.1109/fmpc.1999.750599openalex-snapshot
Data sieving and collective I/O in ROMIO
inferredcites →https://doi.org/10.1109/icppw.2014.47openalex-snapshot
Improving Data Movement Performance for Sparse Data Patterns on the Blue Gene/Q Supercomputer
inferredcites →https://doi.org/10.1109/pdp.2014.46openalex-snapshot
Multithreaded Two-Phase I/O: Improving Collective MPI-IO Performance on a Lustre File System
inferredcites →https://doi.org/10.1109/sc.1998.10006openalex-snapshot
A Case for Using MPI's Derived Datatypes to Improve I/O Performance
inferredcites →https://doi.org/10.1145/165660.165667openalex-snapshot
Improved parallel I/O via a two-phase run-time access strategy
inferredcites →https://doi.org/10.1145/2063384.2063409openalex-snapshot
Topology-aware data movement and staging for I/O acceleration on Blue Gene/P supercomputing systems
inferredcites →https://doi.org/10.1145/2063384.2063419openalex-snapshot
The IBM Blue Gene/Q interconnection network and message unit
inferredcites →https://doi.org/10.1145/2488551.2488567openalex-snapshot
Optimized process placement for collective I/O operations
inferredcites →https://doi.org/10.1145/301816.301826openalex-snapshot
On implementing MPI-IO portably and with high performance
inferredcites →https://doi.org/10.5555/509058.509059openalex-snapshot
A Case for Using MPI's Derived Datatypes to Improve I/O Performance

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-3-030-02465-9_1openalex-snapshot
Analyzing the I/O Scalability of a Parallel Particle-in-Cell Code
inferred← citeshttps://doi.org/10.1007/978-3-030-10549-5_11openalex-snapshot
A Methodology for Handling Data Movements by Anticipation: Position Paper
inferred← citeshttps://doi.org/10.1007/978-3-319-72971-8_3openalex-snapshot
Periodic I/O Scheduling for Super-Computers
inferred← citeshttps://doi.org/10.1007/978-3-319-96983-1_15openalex-snapshot
Interference-Aware Scheduling Using Geometric Constraints
inferred← citeshttps://doi.org/10.1007/s10951-021-00685-8openalex-snapshot
Scheduling periodic I/O access with bi-colored chains: models and algorithms
inferred← citeshttps://doi.org/10.1109/cluster.2017.80openalex-snapshot
TAPIOCA: An I/O Library for Optimized Topology-Aware Data Aggregation on Large-Scale Supercomputers
inferred← citeshttps://doi.org/10.1109/hipc.2017.00034openalex-snapshot
Reducing Network Congestion and Synchronization Overhead During Aggregation of Hierarchical Data
inferred← citeshttps://doi.org/10.1109/nas.2017.8026869openalex-snapshot
Performance Evaluation and Modeling of HPC I/O on Non-Volatile Memory
inferred← citeshttps://doi.org/10.1109/tpds.2017.2703149openalex-snapshot
Trends in Data Locality Abstractions for HPC Systems
inferred← citeshttps://doi.org/10.1145/3205289.3205316openalex-snapshot
Optimizing Data Aggregation by Leveraging the Deep Memory Hierarchy on Large-scale Systems
inferred← citeshttps://doi.org/10.1145/3338510openalex-snapshot
I/O Scheduling Strategy for Periodic Applications
inferred← citeshttps://doi.org/10.1145/3611007openalex-snapshot
I/O Access Patterns in HPC Applications: A 360-Degree Survey
inferred← citeshttps://doi.org/10.1587/transinf.2018edp7297openalex-snapshot
High-Performance End-to-End Integrity Verification on Big Data Transfer
inferred← citeshttps://doi.org/10.2172/1372103openalex-snapshot
2016 Annual Report - Argonne Leadership Computing Facility
inferred← citeshttps://doi.org/10.48550/arxiv.1702.06900openalex-snapshot
Periodic I/O scheduling for super-computers
inferred← citeshttps://doi.org/10.70675/cc3d6340z8abdz44b8zb86cz0170b57c9307openalex-snapshot
Resilient scheduling algorithms for large-scale platforms
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