[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Shu et al., 2021 - Google Patents

Bootstrapping in-situ workflow auto-tuning via combining performance models of component applications

Shu et al., 2021

View PDF
Document ID
2742782973138595868
Author
Shu T
Guo Y
Wozniak J
Ding X
Foster I
Kurc T
Publication year
Publication venue
Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

External Links

Snippet

In an in-situ workflow, multiple components such as simulation and analysis applications are coupled with streaming data transfers. The multiplicity of possible configurations necessitates an auto-tuner for workflow optimization. Existing auto-tuning approaches are …
Continue reading at web.cels.anl.gov (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for programme control, e.g. control unit
    • G06F9/06Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46Multiprogramming arrangements
    • G06F9/50Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061Partitioning or combining of resources
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for programme control, e.g. control unit
    • G06F9/06Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46Multiprogramming arrangements
    • G06F9/50Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386Retrieval requests
    • G06F17/30424Query processing
    • G06F17/30533Other types of queries
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/34Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3409Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F8/00Arrangements for software engineering
    • G06F8/40Transformations of program code
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F2201/00Indexing scheme relating to error detection, to error correction, and to monitoring
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Zhang et al. Restune: Resource oriented tuning boosted by meta-learning for cloud databases
Lee et al. Autonomic machine learning platform
Ardalani et al. Cross-architecture performance prediction (XAPP) using CPU code to predict GPU performance
Marathe et al. Performance modeling under resource constraints using deep transfer learning
Bacardit et al. Large scale data mining using genetics-based machine learning
Shu et al. Bootstrapping in-situ workflow auto-tuning via combining performance models of component applications
Hou et al. Auto-tuning strategies for parallelizing sparse matrix-vector (spmv) multiplication on multi-and many-core processors
Deelman et al. PANORAMA: An approach to performance modeling and diagnosis of extreme-scale workflows
Chen et al. Adaptive optimization of sparse matrix-vector multiplication on emerging many-core architectures
Arnaiz-González et al. MR-DIS: democratic instance selection for big data by MapReduce
Gu et al. Improving execution concurrency of large-scale matrix multiplication on distributed data-parallel platforms
Hua et al. Hadoop configuration tuning with ensemble modeling and metaheuristic optimization
Ganapathi Predicting and optimizing system utilization and performance via statistical machine learning
Cheng et al. Tuning configuration of apache spark on public clouds by combining multi-objective optimization and performance prediction model
Jiang et al. Fast parallel Bayesian network structure learning
Souza et al. Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability
Chen et al. Optimizing sparse matrix-vector multiplication on emerging many-core architectures
Du et al. Monkeyking: Adaptive parameter tuning on big data platforms with deep reinforcement learning
Abdelhafez et al. Mirage: Machine learning-based modeling of identical replicas of the jetson agx embedded platform
Rejitha et al. Energy prediction of CUDA application instances using dynamic regression models
Shu et al. In-situ workflow auto-tuning via combining performance models of component applications
Singh et al. Modular performance prediction for scientific workflows using machine learning
do Rosario et al. Fast selection of compiler optimizations using performance prediction with graph neural networks
Patel et al. Preliminary Scaling Characterization of TPCx-AI
Assogba et al. PredictDDL: Reusable Workload Performance Prediction for Distributed Deep Learning