Name
Title | ||
|---|---|---|
Reducing the Training Overhead of the HPC Compression Autoencoder via Dataset Proportioning |
Abstract | ||
|---|---|---|
As the storage overhead of high-performance computing (HPC) data reaches into the petabyte or even exabyte scale, it could be useful to find new methods of compressing such data. The compression autoencoder (CAE) has recently been proposed to compress HPC data with a very high compression ratio. However, this machine learning-based method suffers from the major drawback of lengthy training time. I... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/NAS51552.2021.9605407 | 2021 IEEE International Conference on Networking, Architecture and Storage (NAS) |
Keywords | DocType | ISBN |
Training,Learning systems,Conferences,Testing | Conference | 978-1-7281-7744-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tong Liu | 1 | 0 | 0.34 |
| Shakeel Alibhai | 2 | 0 | 0.34 |
| Jinzhen Wang | 3 | 1 | 2.38 |
| Qing Liu | 4 | 0 | 0.34 |
| Xubin He | 5 | 0 | 0.34 |