Welcome to visit the Parallel and Distributed Systems Laboratory (PADSYS Lab) website. We are one of the labs in the Department of Computer Science and Engineering, The University of California, Merced.
We are interested in understanding, modeling, designing, and developing real computer systems. Our current research interests include parallel and distributed computing, high-performance interconnects, advanced I/O technologies, Big Data Analytics, Virtualization, Cloud Computing, High-Performance Computing, and Deep Learning system software. We are also closely working with application domain scientists to solve grand challenges facing different communities, such as agriculture, neuroscience, and so on.
Join Us
We are looking forward to working with self-motivated Ph.D., M.S., and undergraduate students, with research interests in parallel and distributed computing systems. Ph.D. students will be supported by full scholarships to cover the tuition fees and living expenses.
Please feel free to contact Prof. Xiaoyi Lu if you have interest.
Links
You can find any of our research projects here.
You can find any of our publications here.
You can find any of our developed software here.
Lab Photos
Recent Publications
- An Optimized Error-controlled MPI Collective Framework Integrated with Lossy Compression
- High-Speed Data Communication with Advanced Networks in Large Language Model Training
- Compression Analysis for BlueField-2/3 Data Processing Units: Lossy and Lossless Perspectives
- Accelerating Lossy and Lossless Compression on Emerging BlueField DPU Architectures
- NVMe-oPF: Designing Efficient Priority Schemes for NVMe-over-Fabrics with Multi-Tenancy Support
- Performance Characterization of Large Language Models on High-Speed Interconnects
- SBGT: Scaling Bayesian-based Group Testing for Disease Surveillance
- A Study of Database Performance Sensitivity to Experiment Settings
- NVMe-oAF: Towards Adaptive NVMe-oF for IO-Intensive Workloads on HPC Cloud
- HatRPC: Hint-Accelerated Thrift RPC over RDMA
- DStore: A Fast, Tailless, and Quiescent-Free Object Store for PMEM
- Understanding the Idiosyncrasies of Real Persistent Memory
- RDMP-KV: Designing Remote Direct Memory Persistence Based Key-Value Stores with PMEM
- INEC: Fast and Coherent in-Network Erasure Coding
- TriEC: Tripartite Graph Based Erasure Coding NIC Offload
- SimdHT-Bench: Characterizing SIMD-Aware Hash Table Designs on Emerging CPU Architectures.
- UMR-EC: A Unified and Multi-Rail Erasure Coding Library for High-Performance Distributed Storage Systems.
- C-GDR: High-Performance Container-Aware GPUDirect MPI Communication Schemes on RDMA Networks.
Recent News
- Multiple New Students Joined PADSYS in 2023! Welcome!
- Dr. Lu and Alex have received recognition at UC Merced FACTS 2023!
- Xiaoyi serves as TPCs/Chairs for multiple 2023/2024 conferences and workshops!
- PADSYS students are starting summer internships in industry and research labs!
- We're thrilled to welcome Dr. Weicong Chen to our team at PADSYS Lab!