41 citations · 62 across the 6 of their papers we have counts for
6 papers
Exceeding the Numerical and Performance Characteristics of IEEE-754 SGEMM with BFloat16 Tensor Cores on GPUs for Scientific Computing
Harun Bayraktar, Cole Brower, John Gunnels +9
Largely due to their increased native capacity for numerical intensity and power efficiency, reduced-precision floating-point computing resources, primarily used in artificial inte…
A Study on the Intersection of GPU Utilization and CNN Inference
Jack Kosaian, Amar Phanishayee
There has been significant progress in developing neural network architectures that both achieve high predictive performance and that also achieve high application-level inference…
ECRM: Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding
Kaige Liu, Jack Kosaian, K. V. Rashmi
Deep-learning-based recommendation models (DLRMs) are widely deployed to serve personalized content to users. DLRMs are large in size due to their use of large embedding tables, an…
Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs
Jack Kosaian, K. V. Rashmi
Neural networks (NNs) are increasingly employed in safety-critical domains and in environments prone to unreliability (e.g., soft errors), such as on spacecraft. Therefore, it is c…
Parity Models: A General Framework for Coding-Based Resilience in ML Inference
Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
Machine learning models are becoming the primary workhorses for many applications. Production services deploy models through prediction serving systems that take in queries and ret…
Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation
Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
Machine learning algorithms are typically run on large scale, distributed compute infrastructure that routinely face a number of unavailabilities such as failures and temporary slo…