Nothing Special   »   [go: up one dir, main page]

Miao et al., 2020 - Google Patents

Cuwide: Towards efficient flow-based training for sparse wide models on gpus

Miao et al., 2020

View PDF
Document ID
267314019545100302
Author
Miao X
Ma L
Yang Z
Shao Y
Cui B
Yu L
Jiang J
Publication year
Publication venue
IEEE Transactions on Knowledge and Data Engineering

External Links

Snippet

Wide models such as generalized linear models and factorization-based models have been extensively used in various predictive applications, eg, recommendation, CTR prediction, and image recognition. Due to the memory bounded property of the models, the …
Continue reading at hsword.github.io (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
    • G06F9/48Programme initiating; Programme switching, e.g. by interrupt
    • G06F9/4806Task transfer initiation or dispatching
    • G06F9/4843Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/4881Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
    • 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
    • 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
    • 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
    • G06F9/30Arrangements for executing machine-instructions, e.g. instruction decode
    • G06F9/38Concurrent instruction execution, e.g. pipeline, look ahead
    • G06F9/3885Concurrent instruction execution, e.g. pipeline, look ahead using a plurality of independent parallel functional units
    • G06F9/3889Concurrent instruction execution, e.g. pipeline, look ahead using a plurality of independent parallel functional units controlled by multiple instructions, e.g. MIMD, decoupled access or execute
    • 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
    • G06F9/30Arrangements for executing machine-instructions, e.g. instruction decode
    • G06F9/30003Arrangements for executing specific machine instructions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • 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
    • G06F8/41Compilation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00Methods or arrangements for processing data by operating upon the order or content of the data handled
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00Digital computers in general; Data processing equipment in general
    • G06F15/76Architectures of general purpose stored programme computers
    • 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

Similar Documents

Publication Publication Date Title
Ben-Nun et al. Demystifying parallel and distributed deep learning: An in-depth concurrency analysis
Jiang et al. Xdl: an industrial deep learning framework for high-dimensional sparse data
Catanzaro et al. Fast support vector machine training and classification on graphics processors
Cano et al. Speeding up the evaluation phase of GP classification algorithms on GPUs
Herrero-Lopez et al. Parallel multiclass classification using SVMs on GPUs
Wang et al. Deep learning at scale and at ease
Gómez-Luna et al. Evaluating machine learningworkloads on memory-centric computing systems
Dong et al. Characterizing the microarchitectural implications of a convolutional neural network (cnn) execution on gpus
Li et al. SaberLDA: Sparsity-aware learning of topic models on GPUs
Gadiyar et al. Artificial intelligence software and hardware platforms
Altinigneli et al. Massively parallel expectation maximization using graphics processing units
Jena et al. High-performance computing and its requirements in deep learning
Ji et al. Accelerating DBSCAN algorithm with AI chips for large datasets
Miao et al. Cuwide: Towards efficient flow-based training for sparse wide models on gpus
Piao et al. Enabling large batch size training for dnn models beyond the memory limit while maintaining performance
Li et al. Deep learning and machine learning with gpgpu and cuda: Unlocking the power of parallel computing
Pan et al. G-slide: A gpu-based sub-linear deep learning engine via lsh sparsification
You et al. Runtime data layout scheduling for machine learning dataset
Wei et al. Deploying and scaling distributed parallel deep neural networks on the Tianhe-3 prototype system
Chandrashekhar et al. Performance analysis of sequential and parallel programming paradigms on CPU-GPUS cluster
Page Scalability of irregular problems
Quesada-Barriuso et al. Selecting the best tridiagonal system solver projected on multi-core CPU and GPU platforms
Wang et al. SingleCaffe: an efficient framework for deep learning on a single node
Kozawa et al. Parallel canopy clustering on GPUs
Fazlali et al. GPU-based Parallel Technique for Solving the N-Similarity Problem in Textual Data Mining