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Produktbild: Advanced Parallel Processing Technologies
Band 16062 - 11%

Advanced Parallel Processing Technologies 16th International Symposium, APPT 2025, Athens, Greece, July 13-16, 2025, Proceedings

Aus der Reihe Lecture Notes in Computer Science
11% sparen

69,99 € UVP 79,17 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.11.2025

Abbildungen

XVII, 241 illus., 232 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen

Herausgeber

Chao Li + weitere

Verlag

Springer Singapore

Seitenzahl

441

Maße (L/B/H)

23,5/15,5/2,5 cm

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9510-20-7

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.11.2025

Abbildungen

XVII, 241 illus., 232 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen

Herausgeber

Verlag

Springer Singapore

Seitenzahl

441

Maße (L/B/H)

23,5/15,5/2,5 cm

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9510-20-7

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Advanced Parallel Processing Technologies

  • .- Best Paper Candidates

    .- DACO: Unlocking Latent Dataflow Opportunities in Edge-side SIMT Accelerators 

    .- ATLAS: Efficient Dynamic GNN System through Abstraction-Driven Incremental Execution

    .- Segmentation-Aware Optimization of Collective for Waferscale Chips 

    .- Area-Efficient Automated Logic Design with Monte-Carlo Tree Search

    .- Chip and Accelerators

    .- NFMap: Node Fusion Optimization for Efficient CGRA Mapping with Reinforcement Learning

    .- A Unified Synthesis Framework for Dataflow Accelerators through Multi-Level Software and Hardware Intermediate Representations

    .- Defect-aware Task Scheduling and Mapping for Redundancy-Enhanced Spatial Accelerators

    .- Irregular Sparsity-Enabled Search-In-Memory Engine for Accelerating Spiking Neural Networks

    .- Memory and Storage

    .- QRAMsim: Efficiently Simulating, Analyzing, and Optimizing Large-scale Quantum Random Access Memory  

    .- CeDMA: Enhancing Memory Efficiency of Heterogeneous Accelerator Systems Through Central DMA Controlling   

    .- PAMM: Adaptive Memory Management for CXL-/UB-Based Heterogeneous Memory Pooling Systems

    .- STAMP: Accelerating Second-order DNN Training Via ReRAM-based Processing-in-Memory Architecture

    .- Cloud and Networking

    .- Cochain: Architectural Support Mechanism for Blockchain-based Task Scheduling

    .- DyQNet: Optimizing Dynamic Entanglement Routing with Online Request in Quantum Network

    .- Veyth: Adaptive Container Placement for Optimizing Cross-Server Network Traffic of Microservice Applications

    .- Design for LLM and ML/AI

    .- Unifying Two Operators with One PIM: Leveraging Hybrid Bonding for Efficient LLM Inference

    .- AsymServe: Demystifying and Optimizing LLM Serving Efficiency on CPU Acceleration Units 

    .- SparseTem: Boosting the Efficiency of CNN-Based Video Encoders by Exploiting Temporal Continuity 

    .- TokenSim: Enabling Hardware and Software Exploration for Large Language Model Inference Systems

    .- Big Data and Graph Processing

    .- Achieving Efficient Temporal Graph Transformation on the GPU

    .- GASgraph: A GPU-accelerated Streaming Graph Processing System based on SubHPMAs 

    .- Accelerating Large-Scale Out-of-GPU-Core GNN Training with Two-Level Historical Caching

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    .- Spatial-Aware Orchestration of LLM Attention on Waferscale Chips

    .- ACLP: Towards More Accurate Loop Prediction for High-Performance Processors

    .- DSL-SGD: Distributed Local Stochastic Gradient Descent with Delayed Synchronization 

    .- Exploiting Large Language Models for Software-Defined Solid-State Drives Design

    .- Comber: QoS-aware and Efficient Deployment for Co-located Microservices and Best-Effort Tasks in Disaggregated Datacenters

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    .- Lembda: Optimizing LLM Inference on Embedded Platforms via CPU/FPGA Co-Processing                                                  

    .- QDLoRA: Enhanced LoRA Fine-Tuning on Quantized LLMs via Integrated Low-Rank Decomposition.