Parallel Computing Cluster for Solving Computational Problems in Data: A Framework for High-Throughput Data Processing - Softcover
Synopsis
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 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.
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Parallel Computing Cluster for Solving Computational Problems in Data
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Parallel Computing Cluster for Solving Computational Problems in Data
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Parallel Computing Cluster for Solving Computational Problems in Data: A Framework for High-Throughput Data Processing
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LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6209340733
ISBN 13: 9786209340734
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Parallel Computing Cluster for Solving Computational Problems in Data (Paperback)
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LAP Lambert Academic Publishing, 2025
ISBN 10: 6209340733
ISBN 13: 9786209340734
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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 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9786209340734
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Parallel Computing Cluster for Solving Computational Problems in Data
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Parallel Computing Cluster for Solving Computational Problems in Data
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Parallel Computing Cluster for Solving Computational Problems in Data
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Parallel Computing Cluster for Solving Computational Problems in Data
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Parallel Computing Cluster for Solving Computational Problems in Data (Paperback)
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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 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9786209340734
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Parallel Computing Cluster for Solving Computational Problems in Data
Published by
LAP LAMBERT Academic Publishing Dez 2025, 2025
ISBN 10: 6209340733
ISBN 13: 9786209340734
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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 parallelism 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. Seller Inventory # 9786209340734