Zihao Liang - Performance Analyst at IBM

Zihao Liang

Performance Analyst at IBM - State Street account

Overland Park, Kansas, United States

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Summary

👤

Senior

🎓

Top School

Zihao Liang is a performance analyst with 8+ years of hands-on experience optimizing large-scale enterprise IT environments and managing performance data for over 10,000 servers. He combines strong analytical skills and scripting expertise (Perl/AWK/Python) to automate data ingestion, cleanse hundreds of gigabytes daily, and power a SAS-backed reporting portal used by application and system owners. Zihao has deep troubleshooting experience across servers, SAN, network and virtualization platforms (VMware, PowerVM, Oracle LDoms) and routinely uses packet-level tools like tcpdump to isolate complex issues. He also brings an operations research and economics background, and contributes to open-source compiler work—improving scheduling in Google's XLS project by replacing solvers and optimizing pipeline register usage—showing a rare blend of systems performance, data engineering, and compiler-level optimization.

8 years of coding experience

Bachelor of Science (B.S.), Mathematics, Bachelor of Science (B.S.), Mathematics at Nanchang University

Master of Science (M.Sc.), Operations Research, Master of Science (M.Sc.), Operations Research at Institute of Systems Sciences, Chinese Academy of Sciences, Beijing 100080, China

University of Minnesota Twin Cities

Github Skills (10)

scheduling10

algorithms10

linear-programming10

compiler-design10

c-language10

sched10

cprogramming-language10

scheduler10

open-source9

verilog8

Programming languages (3)

C++HTMLMLIR

Github contributions (5)

google/xls

May 2022 - Jul 2022

XLS: Accelerated HW Synthesis

Role in this project:

Back-end Developer / System Architect

Contributions:6 commits, 2 PRs, 8 comments in 1 month

Contributions summary:Zihao primarily focused on enhancing the XLS compiler's scheduling capabilities. They implemented a scheduling strategy using the SDC (Static Delay Calculation) formulation to minimize register usage. The user refactored the code, replaced the external dependencies on SCIP/GLPK with GLOP (Google Linear Optimization Package), and added support for sink nodes to improve the efficiency and correctness of the scheduling process. The work involved modifying core scheduling algorithms and data structures related to pipeline optimization.

high-level-synthesisxlsacceleratedsynthesiscompiler

zihaoli-cn/compiler-paper-reading

Oct 2021 - Aug 2022

Contributions:62 commits, 58 pushes, 4 comments in 9 months

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