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Found 5 papers, 3 papers with code
Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach
By leveraging this feature, C4 can rapidly identify the faulty components, swiftly isolate the anomaly, and restart the task, thereby avoiding resource wastage caused by delays in anomaly detection.
FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving
Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving.
MIGPerf: A Comprehensive Benchmark for Deep Learning Training and Inference Workloads on Multi-Instance GPUs
The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts.
Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads
With widespread advances in machine learning, a number of large enterprises are beginning to incorporate machine learning models across a number of products.
Distributed, Parallel, and Cluster Computing
Balanced Sparsity for Efficient DNN Inference on GPU
However, it requires the customization of hardwares to speed up practical inference.
