Automatic VM Allocation for Scientific Application. - Sarunya Pumma; Tiranee Achalakul; Xiaorong Li (ICPADS, 2012)

Towards Scalable Deep Learning via I/O Analysis and Optimization. - Sarunya Pumma; Min Si; Wu-chun Feng; Pavan Balaji (HPCC/SmartCity/DSS, 2017)

Parallel I/O Optimizations for Scalable Deep Learning. - Sarunya Pumma; Min Si; Wu-chun Feng; Pavan Balaji (ICPADS, 2017)

PaPar: A Parallel Data Partitioning Framework for Big Data Applications. - Hao Wang 002; Jing Zhang 039; Da Zhang 004; Sarunya Pumma; Wu-chun Feng (IPDPS, 2017)

A runtime estimation framework for ALICE. - Sarunya Pumma; Wu-chun Feng; Phond Phunchongharn; Sylvain Chapeland; Tiranee Achalakul (Future Gener. Comput. Syst., 2017)

Scalable Deep Learning via I/O Analysis and Optimization. - Sarunya Pumma; Min Si; Wu-Chun Feng; Pavan Balaji (ACM Trans. Parallel Comput., 2019)

Alleviating Load Imbalance in Data Processing for Large-Scale Deep Learning. - Sarunya Pumma; Daniele Buono; Fabio Checconi; Xinyu Que; Wu-chun Feng (CCGRID, 2020)

A Feasibility Study for MPI over HDFS. - Wu-chun Feng; Da Zhang 004; Jing Zhang 039; Kaixi Hou; Sarunya Pumma; Hao Wang 002 (HPEC, 2020)

Scalability Analysis and Optimization for Large-Scale Deep Learning. - Sarunya Pumma (2020)

Semantic-Aware Lossless Data Compression for Deep Learning Recommendation Model (DLRM). - Sarunya Pumma; Abhinav Vishnu (MLHPC@SC, 2021)

RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure. - 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 (MLSys, 2023)

Toward 100TB Recommendation Models with Embedding Offloading. - Intaik Park; Ehsan K. Ardestani; Damian Reeves; Sarunya Pumma; Henry Tsang; Levy Zhao; Jian He; Joshua Deng; Dennis Van Der Staay; Yu Guo; Paul Zhang (RecSys, 2024)