3 papers
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.AR2026
Diagnosing FP4 inference: a layer-wise and block-wise sensitivity analysis of NVFP4 and MXFP4
Musa Cim, Burak Topcu, Mahmut Taylan Kandemir
Quantization addresses the high resource demand for large language models (LLMs) by alleviating memory pressure and bandwidth congestion and providing significantly scaled compute…
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…