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2020 – today
- 2024
[c9]Intaik Park
, Ehsan Ardestani, Damian Reeves, Sarunya Pumma, Henry Tsang, Levy Zhao, Jian He, Joshua Deng, Dennis Van Der Staay, Yu Guo, Paul Zhang:
Toward 100TB Recommendation Models with Embedding Offloading. RecSys 2024: 841-843- 2023
- [c8]Mark Zhao, Dhruv Choudhary, Devashish Tyagi, Ajay Somani, Max Kaplan, Sung-Han Lin, Sarunya Pumma, Jongsoo Park, Aarti Basant, Niket Agarwal, Carole-Jean Wu, Christos Kozyrakis:
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure. MLSys 2023 - 2022
- [i1]Mark Zhao, Dhruv Choudhary, Devashish Tyagi, Ajay Somani, Max Kaplan, Sung-Han Lin, Sarunya Pumma, Jongsoo Park, Aarti Basant, Niket Agarwal, Carole-Jean Wu, Christos Kozyrakis:
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure. CoRR abs/2211.05239 (2022) - 2021
- [c7]Sarunya Pumma, Abhinav Vishnu:
Semantic-Aware Lossless Data Compression for Deep Learning Recommendation Model (DLRM). MLHPC@SC 2021: 1-8 - 2020
- [b1]Sarunya Pumma:
Scalability Analysis and Optimization for Large-Scale Deep Learning. Virginia Tech, Blacksburg, VA, USA, 2020 - [c6]Sarunya Pumma, Daniele Buono, Fabio Checconi, Xinyu Que, Wu-chun Feng:
Alleviating Load Imbalance in Data Processing for Large-Scale Deep Learning. CCGRID 2020: 262-271 - [c5]Wu-chun Feng, Da Zhang, Jing Zhang, Kaixi Hou, Sarunya Pumma, Hao Wang:
A Feasibility Study for MPI over HDFS. HPEC 2020: 1-7
2010 – 2019
- 2019
- [j2]Sarunya Pumma, Min Si, Wu-Chun Feng, Pavan Balaji:
Scalable Deep Learning via I/O Analysis and Optimization. ACM Trans. Parallel Comput. 6(2): 6:1-6:34 (2019) - 2017
- [j1]Sarunya Pumma, Wu-chun Feng, Phond Phunchongharn, Sylvain Chapeland, Tiranee Achalakul:
A runtime estimation framework for ALICE. Future Gener. Comput. Syst. 72: 65-77 (2017) - [c4]Sarunya Pumma, Min Si, Wu-chun Feng, Pavan Balaji:
Towards Scalable Deep Learning via I/O Analysis and Optimization. HPCC/SmartCity/DSS 2017: 223-230 - [c3]Sarunya Pumma, Min Si, Wu-chun Feng, Pavan Balaji:
Parallel I/O Optimizations for Scalable Deep Learning. ICPADS 2017: 720-729 - [c2]Hao Wang, Jing Zhang, Da Zhang, Sarunya Pumma, Wu-chun Feng:
PaPar: A Parallel Data Partitioning Framework for Big Data Applications. IPDPS 2017: 605-614 - 2012
- [c1]Sarunya Pumma, Tiranee Achalakul, Xiaorong Li:
Automatic VM Allocation for Scientific Application. ICPADS 2012: 828-833
Coauthor Index
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