Aug 2026· Cluster Computing· Vol 29· 0 citations· 76 references
Computer Science
TL;DR
This work presents PROFET, a system that predicts the training latency of arbitrary Convolutional Neural Network implementations across a wide range of GPU types and mini-batch sizes and introduces a novel operation-name clustering heuristic that effectively resolves naming inconsistencies in computational graphs where semantically similar operations are labeled differently.
The applicability of PEAT is demonstrated by presenting the results and analysis using GPUs from the two most popular vendors, NVIDIA V100 and AMD MI250, on various AI models, from vision tasks to language models, for both pretraining and finetuning scenarios.
Xuan Truong Nguyen Department of Next Generation Semico Convergence, Open Sharing System, Seoul National University et al.· 0 citations
A deep learning-based failure prediction model that integrates Convolutional Neural Networks and Bidirectional Long Short-Term Memory networks to identify job failures before they occur is presented, improving the performance of cloud computing applications by reducing job failures and optimising resource utilisation.
Wunukhen Shehu Awudu, P. Asuquo, B. Agbor et al.· E3S Web of Conferences· 0 citations
A dynamic recurrent neural network is proposed to accurately predict workloads and integrates an auto-encoder to effectively extract representations from the original workload data with high dimensionality to enable adaptive and accurate predictions for highly variable workloads.
Okore Kalu, C. Okafor, P. Asuquo et al.· E3S Web of Conferences· 0 citations
With the explosive development of LLM-empowered agent technology, LLM inference performance has become more important than training. Cloud computing is a popular deployment approach, where performance prediction is vital for instance selection and QoS assurance. However, prediction is challenging due to GPU hardware he...
Huan Zhou, Zhi-Peng Wang, Meng-Juan Li et al.· Fall Joint Computer Conferen...· 0 citations
A workload characterization study of 13 419 CNN configurations on two GPU platforms under GPU telemetry reveals that energy, latency, and memory exhibit fundamentally distinct scaling behaviors: energy and latency diverge by 3x under high computational demand, and cross-GPU transferability differs by target.
Conventional compute-in-memory (CIM) deployment flows usually assume a fixed crossbar geometry, although DNN layers often have different channel counts and matrix shapes. The resulting shape mismatch leaves part of the array capacity unused and can increase the number of split-and-transfer operations during inference....
Kaiwen Deng, Sifan Sun, Hanjie Liu et al.· IEEE Non-Volatile Memory Sys...· 0 citations
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