research
∙
07/07/2023
CODAG: Characterizing and Optimizing Decompression Algorithms for GPUs
Data compression and decompression have become vital components of big-d...
share
research
∙
01/09/2023
Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU
We introduce Stream-K, a work-centric parallelization of matrix multipli...
research
∙
08/31/2022
Efficient Sparsely Activated Transformers
Transformer-based neural networks have achieved state-of-the-art task pe...
research
∙
03/09/2022
GPU-Initiated On-Demand High-Throughput Storage Access in the BaM System Architecture
Graphics Processing Units (GPUs) have traditionally relied on the host C...
research
∙
11/06/2019
A Programmable Approach to Model Compression
Deep neural networks frequently contain far more weights, represented at...
research
∙
07/19/2019
GPU-Accelerated Atari Emulation for Reinforcement Learning
We designed and implemented a CUDA port of the Atari Learning Environmen...
research
∙
12/06/2017