A list of pieces of Google infrastructure mentioned in papers. (Almost certainly out of date and inaccurate because it is based on what the papers said at the time they were written.)
Bazel
Build system
Bigtable
ClangMR
Code refactoring infrastructure
Clipper
Find dead code
CodeSearch
Critique
Code review tool [sadowski:icse-seip:2018]
ErrorProne
Static analysis (under TriCorder)
MapReduce
Megastore
Piper
Distributed version control system optimized for huge monorepos
Rosie
Splits large changes across multiple reviewing boundaries (for monorepos)
Spanner
Distributed, timestamped database (replaces BigTable?)
Tricorder
Static analysis framework
TrueTime
Time server returning time intervals based on GPS and atomic clocks
Papers related to Google papers and infrastructure
- TensorFlow: Large-scale machine learning on heterogeneous distributed systems [abadi:arxiv:2016]
- Bigtable: A distributed storage system for structured data [chang:tocs:2012]
- Spanner: Google's globally distributed database [corbett:tocs:2013]
- The tail at scale [dean:cacm:2013]
- Fast sparse ConvNets [elsen:arxiv:2019]
- Rigging the lottery: Making all tickets winners [evci:arxiv:2021]
- Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity [fedus:arxiv:2021]
- The state of sparsity in deep neural networks [gale:arxiv:2019]
- Sparse GPU kernels for deep learning [gale:arxiv:2020]
- Cores that don't count [hochschild:hotos:2021]
- Code coverage at Google [ivankovic:fse:2019]
- In-datacenter performance analysis of a tensor processing unit [jouppi:isca:2017]
- GShard: Scaling giant models with conditional computation and automatic sharding [lepikhin:arxiv:2020]
- API usability at scale [macvean:ppig:2016]
- Why Google stores billions of lines of code in a single repository [potvin:cacm:2016]
- Lessons from building static analysis tools at Google [sadowski:cacm:2018]
- Modern code review: A case study at Google [sadowski:icse-seip:2018]
- Tricorder: Building a program analysis ecosystem [sadowski:icse:2015]
- Outrageously large neural networks: The sparsely-gated mixture-of-experts layer [shazeer:arxiv:2017]
- Attention is all you need [vaswani:arxiv:2017]
- Large-scale automated refactoring using ClangMR [wright:icsm:2013]