11 citations · 16 across the 6 of their papers we have counts for
6 papers
Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training
Jared Fernandez, Luca Wehrstedt, Leonid Shamis +5
Dramatic increases in the capabilities of neural network models in recent years are driven by scaling model size, training data, and corresponding computational resources. To devel…
Characterizing and Efficiently Accelerating Multimodal Generation Model Inference
Yejin Lee, Anna Sun, Basil Hosmer +27
Generative artificial intelligence (AI) technology is revolutionizing the computing industry. Not only its applications have broadened to various sectors but also poses new system…
Minuet: Accelerating 3D Sparse Convolutions on GPUs
Jiacheng Yang, Christina Giannoula, Jun Wu +3
Sparse Convolution (SC) is widely used for processing 3D point clouds that are inherently sparse. Different from dense convolution, SC preserves the sparsity of the input point clo…
Large Language Models for Compiler Optimization
Chris Cummins, Volker Seeker, Dejan Grubisic +8
We explore the novel application of Large Language Models to code optimization. We present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly for code…
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
Youwei Liang, Kevin Stone, Ali Shameli +8
Finding the optimal pass sequence of compilation can lead to a significant reduction in program size and/or improvement in program efficiency. Prior works on compilation pass order…
OLLA: Optimizing the Lifetime and Location of Arrays to Reduce the Memory Usage of Neural Networks
Benoit Steiner, Mostafa Elhoushi, Jacob Kahn +1
The size of deep neural networks has grown exponentially in recent years. Unfortunately, hardware devices have not kept pace with the rapidly increasing memory requirements. To cop…