Peng Wang
Distinguished Engineer, HPC Developer Technology at NVIDIA
California, United States
Summary
🤩
Rockstar🎓
Top SchoolPeng Wang is a Distinguished Engineer specializing in porting and performance-optimizing HPC applications for GPU-based supercomputers, drawing on a Ph.D. in Computational Physics from Stanford and over 15 years at NVIDIA across engineering and leadership roles. He blends deep numerical algorithms and parallel programming expertise (CUDA, MPI, OpenMP, C/C++/Fortran) with practical developer enablement—helping teams extract peak performance on current and future GPU architectures. His hands-on work spans from low-level performance tuning to guiding large-scale porting efforts, and he has contributed backend scheduler enhancements to notable cloud-native projects like CNCF’s Volcano. Based in California, he is equally comfortable mentoring teams and diving into code-level optimizations, often surfacing subtle correctness and robustness fixes during large system integrations.
8 years of coding experience
15 years of employment as a software developer
MS Physics, MS Physics at Nankai UniversityPh.D. Computational Physics, Ph.D. Computational Physics at Stanford University
Github Skills (8)
kubernetes10
batch10
go10
batchfile10
batch-processing10
kubernetes-pods10
api-design9
scheduling9
Programming languages (6)
Github contributions (5)
volcano-sh/volcano
Feb 2021 - Aug 2022
A Cloud Native Batch System (Project under CNCF)
Role in this project:
Back-end Developer
Back-end DeveloperContributions:109 reviews, 62 commits, 56 PRs in 1 year 6 months
Contributions summary:Peng primarily contributed to the backend of the "volcano" project, a cloud-native batch system. Their work focused on modifying and extending API structures and scheduler logic, specifically adding and modifying features related to job preemption and resource allocation within the TDM plugin. Key changes include enhancing job management with features such as min pod alive and max eviction steps and adjusting bool checks with strconv.ParseBool for robustness.
golangbigdatabatchmachine-learningkubernetes
wpeng102/ufmclient
Jun 2023 - Jan 2024
The rust client of UFM
Contributions:1 release, 12 pushes, 3 branches in 7 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.