Parallel Computing Cluster Solving by Mutyala Rao (10 results)
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Taschenbuch. Condition: Neu. Parallel Computing Cluster for Solving Computational Problems in Data | A Framework for High-Throughput Data Processing | Chavala Mutyala Rao | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786209340734 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Briviba…s Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.
Language: English
Published by LAP LAMBERT Academic Publishing Dez 2025, 2025
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 56 pp. Englisch.
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Paperback. Condition: new. Paperback. Thisbook tackles a critical bottleneck in large-scale AI: the slow and communication-heavy training of massive Deep Neural Networks (DNNs) on multi-GPU systems. It addresses the trade-off between two main parallelization methods. Data parallelism suffers from severe communication overhead fo…r large models, while pipelined model parallelism (like PipeDream) offers up to 8.91x speedup for large Fully Connected/Recurrent Neural Networks but causes "weight staleness," degrading model accuracy. To resolve this, the paper introduces SpecTrain, a novel technique. SpecTrain uses the momentum from optimizers to predict future weight updates, allowing pipelined computation with accurate, non-stale weights. This enables the high GPU utilization and speed of pipelining while maintaining the training robustness and final accuracy of synchronous methods. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Language: English
Published by LAP LAMBERT Academic Publishing Dez 2025, 2025
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Thisbook tackles a critical bottleneck in large-scale AI: the slow and communication-heavy training of massive Deep Neural Networks (DNNs) on multi-GPU systems. It addresses the trade-off between two main parallelization methods. Data p…arallelism suffers from severe communication overhead for large models, while pipelined model parallelism (like PipeDream) offers up to 8.91x speedup for large Fully Connected/Recurrent Neural Networks but causes 'weight staleness,' degrading model accuracy. To resolve this, the paper introduces SpecTrain, a novel technique. SpecTrain uses the momentum from optimizers to predict future weight updates, allowing pipelined computation with accurate, non-stale weights. This enables the high GPU utilization and speed of pipelining while maintaining the training robustness and final accuracy of synchronous methods.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch.
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