Blake Hechtman
Software Engineer at Google
Redwood City, California, United States
Summary
🤩
Rockstar🎓
Top SchoolBlake Hechtman is a software engineer and computer architect with nine years of experience building high-performance compiler and hardware-software systems, currently working on next-generation SPARC chips at Oracle and previously at Google. He holds a PhD from Duke University focused on GPU parallelism and synchronization, and has a strong track record of low-level optimization in open-source ML compilers such as XLA, JAX, and TensorFlow. His contributions emphasize algebraic simplification, operation canonicalization, and fusion opportunities that measurably improve execution performance on CPUs, GPUs, and accelerators. Based in Redwood City, he blends hardware insight with backend compiler engineering, plus hands-on experience tuning RNG and numerical behavior for mixed-precision ML. An under-the-radar strength is his ability to map research-grade synchronization techniques into practical compiler transformations that accelerate real-world models.
9 years of coding experience
2 years of employment as a software developer
Bachelor of Science (B.S.), Electrical and Computer Engineering, Bachelor of Science (B.S.), Electrical and Computer Engineering at Duke UniversityMiami Palmetto Senior High School
English, German
Github Skills (18)
simplify10
c-language10
python10
machine-learning10
numpy10
compiler-optimization10
xla10
compiler10
jax10
cprogramming-language10
simplification10
tensorflow9
code-generation9
deep-learning8
testing8
Programming languages (4)
Github contributions (5)
openxla/xla
Jan 2017 - Jan 2023
A machine learning compiler for GPUs, CPUs, and ML accelerators
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 reviews, 241 commits, 5 comments in 6 years 1 month
Contributions summary:Blake's contributions primarily revolved around the algebraic simplification of XLA (XLA: XLA: A machine learning compiler for GPUs, CPUs, and ML accelerators). They implemented and improved optimizations involving division, exponentials, and transpose operations within the compiler. The user's work demonstrates a focus on performance improvements by rewriting and canonicalizing operations, as well as enhancing the ability of the compiler to fuse operations for optimized execution. The contributions extend the functionality and capabilities of the XLA compiler.
compilermachine-learning
tensorflow/tensorflow
Jan 2017 - Dec 2022
An Open Source Machine Learning Framework for Everyone
Role in this project:Backend Developer
Backend DeveloperContributions:4 reviews, 263 commits, 15 comments in 6 years
Contributions summary:Blake primarily focused on optimizing the algebraic simplification process within the XLA compiler. They implemented and refined several transformations, including converting reduce operations involving concatenation, transposing dot products, and simplifying select statements. Their contributions directly improved the efficiency and performance of the XLA compiler, leading to potentially faster model execution. The user also made contributions related to the handling of random number generators within the XLA framework.
machine-learningtensorflowpythondeep-learningdeep-neural-networks
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