[1]
S. Li*, S. W. D. Chien*, T. Gao, and M. Honda, “Remote TCP Connection Offload and Applications,” Accepted for publication at USENIX Symposium on Networked Systems Design and Implementation (NSDI '26), 2026.
[2]
S. W. D. Chien, K. Sato, A. Podobas, N. Jansson, S. Markidis, and M. Honda, “ParaLog: Consistent host-side logging for parallel checkpoints,” in Proceedings of the 2025 ACM Symposium on Cloud Computing, 2025, pp. 59-73.
[3]
T. Gao, X. Ma, S. Narreddy, E. Luo, S. W. D. Chien, and M. Honda, “Designing Transport-Level Encryption for Datacenter Networks,” in Proceedings of the 9th Asia-Pacific Workshop on Networking, 2025, pp. 142–149.
[4]
S. Li, S. W. D. Chien, T. Gao, and M. Honda, “Remote TCP Connection Offload with XO,” in Proceedings of the 9th Asia-Pacific Workshop on Networking, 2025, pp. 37–43.
[5]
Z. Chen, S. W. D. Chien, P. Qian, and N. Zilberman, “Detecting Anomalies in Machine Learning Infrastructure via Hardware Telemetry,” arXiv preprint arXiv:2510.26008, 2025.
[6]
S. W. D. Chien, K. Sato, A. Podobas, N. Jansson, S. Markidis, and M. Honda, “Improving Cloud Storage Network Bandwidth Utilization of Scientific Applications,” in Proceedings of the 7th Asia-Pacific Workshop on Networking, 2023, pp. 172–173.
[7]
S. W. D. Chien et al., “NoaSci: A Numerical Object Array Library for I/O of Scientific Applications on Object Storage,” in 2022 30th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), 2022, pp. 172–176.
[8]
S. Li, S. W. D. Chien, and M. Honda, “FlexPort: transport scale-out with modern NICs,” in Proceedings of the 3rd International CoNEXT Student Workshop, 2022, pp. 15–16.
[9]
A. Podobas, W. D. Chien, S. Markidis, M. Flatken, and A. Gerndt, “Workflows to Driving High-Performance Interactive Supercomputing for Urgent Decision Making,” in ISC High Performance Computing, 2022, p. 233.
[10]
M. Svedin, A. Podobas, S. W. D. Chien, and S. Markidis, “Higgs Boson Classification: Brain-inspired BCPNN Learning with StreamBrain,” in 2021 IEEE International Conference on Cluster Computing (CLUSTER), 2021, pp. 705–710.
[11]
N. Brown et al., “Utilising urgent computing to tackle the spread of mosquito-borne diseases,” in 2021 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC), 2021, pp. 36–44.
[12]
A. Podobas et al., “StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs,” in Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies, 2021, pp. 1–6.
[13]
M. Svedin, S. W. D. Chien, G. Chikafa, N. Jansson, and A. Podobas, “Benchmarking the Nvidia GPU Lineage: From Early K80 to Modern A100 with Asynchronous Memory Transfers,” in Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies, 2021, pp. 1–6.
[14]
S. W. D. Chien, A. Podobas, I. B. Peng, and S. Markidis, “tf-Darshan: Understanding Fine-grained I/O Performance in Machine Learning Workloads,” in 2020 IEEE International Conference on Cluster Computing (CLUSTER), 2020, pp. 359–370.
[15]
A. Podobas et al., “StreamBrain: An HPC DSL for Brain-like Neural Networks on Heterogeneous Systems,” in The International Conference for High Performance Computing, Networking, Storage, and Analysis, 2020, no. Poster Session.
[16]
S. W. D. Chien, I. B. Peng, and S. Markidis, “Posit NPB: Assessing the Precision Improvement in HPC Scientific Applications,” in PPAM 2019: Parallel Processing and Applied Mathematics, 2020, pp. 301–310.
[17]
S. W. D. Chien, J. Nylund, G. Bengtsson, I. B. Peng, A. Podobas, and S. Markidis, “sputniPIC: an Implicit Particle-in-Cell Code for Multi-GPU Systems,” in 2020 IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), 2020, pp. 149–156.
[18]
S. W. D. Chien, I. B. Peng, and S. Markidis, “Performance Evaluation of Advanced Features in CUDA Unified Memory,” in 2019 IEEE/ACM Workshop on Memory Centric High Performance Computing (MCHPC), 2019, pp. 50–57.
[19]
N. Brown et al., “The role of interactive super-computing in using hpc for urgent decision making,” in International Conference on High Performance Computing, 2019, pp. 528–540.
[20]
C. P. Sishtla, S. W. D. Chien, V. Olshevsky, E. Laure, and S. Markidis, “Multi-GPU acceleration of the iPIC3D implicit particle-in-cell code,” in International Conference on Computational Science, 2019, pp. 612–618.
[21]
S. W. D. Chien, S. Markidis, V. Olshevsky, Y. Bulatov, E. Laure, and J. Vetter, “TensorFlow doing HPC,” in 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2019, pp. 509–518.
[22]
V. Olshevsky et al., “Automatic classification of plasma regions using 3D energy distributions,” 2019.
[23]
S. Markidis, S. W. D. Chien, and V. Olshevsky, “Accelerating Magnetospheric Modeling with Heterogeneous Hardware,” in AGU Fall Meeting Abstracts, 2019, vol. 2019, pp. SM12B-07.
[24]
S. W. D. Chien et al., “Characterizing deep-learning I/O workloads in TensorFlow,” in 2018 IEEE/ACM 3rd International Workshop on Parallel Data Storage & Data Intensive Scalable Computing Systems (PDSW-DISCS), 2018, pp. 54–63.
[25]
S. Narasimhamurthy et al., “The SAGE project: a storage centric approach for exascale computing,” in Proceedings of the 15th ACM International Conference on Computing Frontiers, 2018, pp. 287–292.
[26]
S. W. D. Chien, S. Markidis, R. Karim, E. Laure, and S. Narasimhamurthy, “Exploring scientific application performance using large scale object storage,” in International Conference on High Performance Computing, 2018, pp. 117–130.
[27]
S. Markidis, S. W. D. Chien, E. Laure, I. B. Peng, and J. S. Vetter, “NVIDIA Tensor Core Programmability, Performance & Precision,” in 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018, pp. 522–531.