6 citations · 19 across the 8 of their papers we have counts for
5 papers · 1 filter
Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput
Arissa Wongpanich, Tayo Oguntebi, Jose Baiocchi Paredes +6
Recent years have seen the emergence of machine learning (ML) workloads deployed in warehouse-scale computing (WSC) settings, also known as ML fleets. As the computational demands…
Accelerating Retrieval-Augmented Language Model Serving with Speculation
Zhihao Zhang, Alan Zhu, Lijie Yang +4
Retrieval-augmented language models (RaLM) have demonstrated the potential to solve knowledge-intensive natural language processing (NLP) tasks by combining a non-parametric knowle…
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao +4
Precise hardware performance models play a crucial role in code optimizations. They can assist compilers in making heuristic decisions or aid autotuners in identifying the optimal…
Learning Large Graph Property Prediction via Graph Segment Training
Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija +5
Learning to predict properties of large graphs is challenging because each prediction requires the knowledge of an entire graph, while the amount of memory available during trainin…
GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation
Ondrej Sykora, Phitchaya Mangpo Phothilimthana, Charith Mendis +1
Analytical hardware performance models yield swift estimation of desired hardware performance metrics. However, developing these analytical models for modern processors with sophis…