Garrett Wang is a Senior Software Engineer at Google with nine years of experience building high-performance infrastructure, profiling tools, and data processing pipelines. He combines deep systems-level expertise—exemplified by contributions to pprof and a C++/cache-optimization research project—with production experience migrating terabyte-scale MapReduce jobs and designing scalable load-testing frameworks. At Google he focuses on performance and efficiency tooling, automated performance insights, and back-end systems for large product surfaces like YouTube social infrastructure. Garrett’s background blends academic research (a system that improved SPEC CPU2006 benchmarks) with pragmatic engineering at scale, including shipping substantial C++ code and designing robust rollback and testing mechanisms. Based in Sunnyvale, he’s comfortable across C++, Java, Python and pipeline frameworks, and brings a knack for translating low-level performance insights into usable developer tooling. Beyond the obvious, he’s proven at both inventing profiling improvements and operationalizing them into teams’ workflows.
9 years of coding experience
6 years of employment as a software developer
High School, Junior, High School, Junior at Kickapoo High School
Bachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at Grinnell College
High School, High School at High School Affiliated to Fudan University
pprof is a tool for visualization and analysis of profiling data
Role in this project:
Back-end Developer / Performance Engineer
Contributions:21 reviews, 21 commits, 35 PRs in 1 year 5 months
Contributions summary:Garrett primarily focused on improving the pprof tool's functionality and usability related to assembly reports and performance analysis. Their contributions included adding support for Intel syntax in assembly reports, which involved changes to the `internal/report`, `internal/binutils`, `internal/driver`, `internal/plugin`, and `internal/driver` packages. They also addressed issues, such as fixing tests and improving error messages. This work demonstrates a strong understanding of the tool's internals and how to improve its performance analysis capabilities.
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