Výsledky vyhľadávania - Theory of computation → Distributed algorithms

On implementing SWMR registers from SWSR registers in systems with Byzantine failures Autor Hu, Xing, Toueg, Sam

“…The implementation of registers from (potentially) weaker registers is a classical problem in the theory of distributed computing. Since Lamport’s pioneering work…”
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IFHE: Intermediate-Feature Heterogeneity Enhancement for Image Synthesis in Data-Free Knowledge Distillation Autor Chen, Yi, Liu, Ning, Ren, Ao, Yang, Tao, Liu, Duo

“…Data-free knowledge distillation (DFKD) explores training a compact student network only by a pre-trained teacher without real data. Prevailing DFKD methods…”
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AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs Autor Zhang, Ruisi, Javaheripi, Mojan, Ghodsi, Zahra, Bleiweiss, Amit, Koushanfar, Farinaz

“…Distributed GNN training on contemporary massive and densely connected graphs requires information aggregation from all neighboring nodes, which leads to an explosion of inter-server communications…”
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Network-Offloaded Bandwidth-Optimal Broadcast and Allgather for Distributed AI Autor Khalilov, Mikhail, Girolamo, Salvatore Di, Chrapek, Marcin, Nudelman, Rami, Bloch, Gil, Hoefler, Torsten

“…In the Fully Sharded Data Parallel (FSDP) training pipeline, collective operations can be interleaved to maximize the communication/computation overlap…”
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Asynchronous Distributed-Memory Parallel Algorithms for Influence Maximization Autor Singhal, Shubhendra Pal, Hati, Souvadra, Young, Jeffrey, Sarkar, Vivek, Hayashi, Akihiro, Vuduc, Richard

“… We propose distributed-memory parallel algorithms for the two main kernels of a state-of-the-art implementation of one IM algorithm, influence maximization via martingales (IMM…”
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Submodularity of Distributed Join Computation Autor Li, Rundong, Riedewald, Mirek, Deng, Xinyan

“…We study distributed equi-join computation in the presence of join-attribute skew, which causes load imbalance…”
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NEO-DNND: Communication-Optimized Distributed Nearest Neighbor Graph Construction Autor Iwabuchi, Keita, Steil, Trevor, Priest, Benjamin W., Pearce, Roger, Sanders, Geoffrey

“…Graph-based approximate nearest neighbor algorithms have shown high neighbor structure representation quality…”
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DS-GL: Advancing Graph Learning via Harnessing Nature's Power within Scalable Dynamical Systems Autor Song, Ruibing, Wu, Chunshu, Liu, Chuan, Li, Ang, Huang, Michael, Geng, Tony Tong

“… problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL…”
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MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training Autor Bae, Jonghyun, Choi, Jong Youl, Pasini, Massimiliano Lupo, Mehta, Kshitij, Zhang, Pei, Ibrahim, Khaled Z.

“…Scalable data management is essential for processing large scientific dataset on HPC platforms for distributed deep learning…”
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Gluon-Async: A Bulk-Asynchronous System for Distributed and Heterogeneous Graph Analytics Autor Dathathri, Roshan, Gill, Gurbinder, Hoang, Loc, Jatala, Vishwesh, Pingali, Keshav, Nandivada, V. Krishna, Dang, Hoang-Vu, Snir, Marc

“…Distributed graph analytics systems for CPUs, like D-Galois and Gemini, and for GPUs, like D-IrGL and Lux, use a bulk-synchronous parallel (BSP…”
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Enumeration of Billions of Maximal Bicliques in Bipartite Graphs without Using GPUs Autor Pan, Zhe, He, Shuibing, Li, Xu, Zhang, Xuechen, Yin, Yanlong, Wang, Rui, Shou, Lidan, Song, Mingli, Sun, Xian-He, Chen, Gang

“… To overcome this limitation, we propose an AdaMBE algorithm. First, we redesign its core operations using local neighborhood information derived from computational subgraphs to minimize redundant memory accesses…”
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BLOwing Trees to the Ground: Layout Optimization of Decision Trees on Racetrack Memory Autor Hakert, Christian, Khan, Asif Ali, Chen, Kuan-Hsun, Hameed, Fazal, Castrillon, Jeronimo, Chen, Jian-Jia

“…Modern distributed low power systems tend to integrate machine learning algorithms, which are directly executed on the distributed devices (on the edge…”
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Accelerating Distributed DLRM Training with Optimized TT Decomposition and Micro-Batching Autor Wang, Weihu, Xia, Yaqi, Yang, Donglin, Zhou, Xiaobo, Cheng, Dazhao

“…Deep Learning Recommendation Models (DLRMs) are pivotal in various sectors, yet they are hindered by the high memory demands of embedding tables and the significant communication overhead in distributed training environments…”
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PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Autor Wang, Yisu, Wu, Ruilong, Li, Xinjiao, Kutscher, Dirk

“… As DNNs and datasets grow, distributed training becomes extremely time-consuming and demands larger clusters…”
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Efficient Algorithm for the Computation of the Solution to a Sparse Matrix Equation in Distributed Control Theory Autor Pedroso, Leonardo, Batista, Pedro

“…In this short communication, an algorithm for efficiently solving a sparse matrix equation, which arises frequently in the field of distributed control and estimation theory, is proposed…”
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A Scalable Algorithm for Active Learning Autor Chen, Youguang, Wen, Zheyu, Biros, George

“…FIRAL is a recently proposed deterministic active learning algorithm for multiclass classification using logistic regression…”
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Reshaping High Energy Physics Applications for Near-Interactive Execution Using TaskVine Autor Sly-Delgado, Barry, Tovar, Ben, Zhou, Jin, Thain, Douglas

“…High energy physics experiments produce petabytes of data annually that must be reduced to gain insight into the laws of nature. Early-stage reduction executes…”
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Enforcing Crash Consistency of Evolving Network Analytics in Non-Volatile Main Memory Systems Autor Lim, Soklong, Lu, Zaixin, Ren, Bin, Zhang, Xuechen

“…Evolving graph processing has enabled the modeling of many complex network systems, e.g., online social networks and gene networks. Existing in-memory graph…”
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Optimizing Distributed ML Communication with Fused Computation-Collective Operations Autor Punniyamurthy, Kishore, Hamidouche, Khaled, Beckmann, Bradford M.

“…Machine learning models are distributed across multiple nodes using numerous parallelism strategies…”
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TorchGT: A Holistic System for Large-Scale Graph Transformer Training Autor Zhang, Meng, Sun, Jie, Hu, Qinghao, Sun, Peng, Wang, Zeke, Wen, Yonggang, Zhang, Tianwei

“… TORCHGT optimizes training at three different levels. At algorithm level, by harnessing the graph sparsity, TORCHGT introduces a Dual-interleaved Attention which is computation-efficient and accuracy-maintained…”
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