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Found 5 papers, 2 papers with code
A Study on the Intersection of GPU Utilization and CNN Inference
Achieving high GPU utilization is critical to increasing application-level throughput and ensuring a good return on investment for deploying GPUs.
Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs
Algorithm-based fault tolerance (ABFT) is emerging as an efficient approach for fault tolerance in NNs.
ECRM: Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding
We present ECRM, a DLRM training system that achieves efficient fault tolerance using erasure coding.
Parity Models: A General Framework for Coding-Based Resilience in ML Inference
In order to scale to high query rates, prediction serving systems are run on many machines in cluster settings, and thus are prone to slowdowns and failures that inflate tail latency and cause violations of strict latency targets.
Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation
To the best of our knowledge, this work proposes the first learning-based approach for designing codes, and also presents the first coding-theoretic solution that can provide resilience for any non-linear (differentiable) computation.
