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cs.DC2026
Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs
Jun-Liang Lin, Kamesh Madduri, Mahmut Taylan Kandemir
Graph foundation models have demonstrated remarkable adaptability across diverse downstream tasks through large-scale pretraining on graphs. However, existing implementations of th…
cs.DC2026
Parallelization Strategies for Dense LLM Deployment: Navigating Through Application-Specific Tradeoffs and Bottlenecks
Burak Topcu, Musa Oguzhan Cim, Poovaiah Palangappa +2
Breakthroughs in the generative AI domain have fueled an explosion of large language model (LLM)-powered applications, whose workloads fundamentally consist of sequences of inferen…