Scholar
Xiaobo Zhou
Google Scholar ID: M50Z43QAAAAJ
University of Macau; UCCS
Citations & Impact
Citations
2,100
H-index
27
i10-index
64
Publications
20
Co-authors
7
list available
Contact
No contact links provided.
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- - [ATC25] Sparse Matrix-Matrix Multiplication on Tensor Cores with Asynchronous and Balanced Kernel Optimization, USENIX ATC, 2025
- - [PPoPP25] Harnessing Inter-GPU Shared Memory for Seamless MoE Communication-Computation Fusion, ACM PPoPP, 2025
- - [SC24] Scaling New Heights: Transformative Cross-GPU Sampling for Training Billion-Edge Graphs, ACM/IEEE SC, 2024
- - [SC24] MCFuser: High-Performance and Rapid Fusion of Memory-bound Compute-intensive Operators, ACM/IEEE SC, 2024
- - [SC24] Accelerating Distributed DLRM Training with Optimized TT Decomposition and Micro-Batching, ACM/IEEE SC, 2024
- - [ATC24] Expeditious High-Concurrency MicroVM SnapStart in Persistent Memory with an Augmented Hypervisor, USENIX ATC, 2024
- - [HPDC23] Let It Go: Relieving Garbage Collection Pain for Latency Critical Applications in Golang, ACM HPDC, 2023
- - [HPDC23] Redundancy-Free High-Performance Dynamic GNN Training with Hierarchical Pipeline Parallelism, ACM HPDC, 2023 (The Best Paper Runner-up)
Research Experience
- - Served as the chair of the IEEE Technical Committee on Distributed Processing (2020-2023)
- - Supervised over a dozen PhD students, with graduates securing tenure-track faculty positions at research-oriented academic institutions, including the University of Texas at San Antonio and the University of North Carolina at Charlotte
- - Former students have also joined prestigious Argonne National Laboratory, and high-tech firms including Amazon, Cloudflare, Intel, Meta, Microsoft, Nvidia, and Tencent
Background
- - Research Interests: Cloud Computing, Systems for Machine Learning, High-Performance Distributed Computing, Memory Systems and OS
- - Professional Field: Advancing system support for data-intensive applications, particularly in the realms of cloud computing and machine learning
- - Brief Introduction: Focuses on optimizing performance, resource efficiency, and system reliability, all of which are critical for scaling modern computational workloads.
