Developer guide

Use this section when implementing, integrating, or verifying QuantEM.GPU. Scientists using notebooks can start with the Python workflow.

Find the task

Task	Start here
Understand an operation’s equations and array contract	Scientific operations
Implement an operation on a device	MPS, CUDA, and other backends
Build a native application	Native integration
Implement a file reader or writer	File formats and codecs
Run a remote CUDA service	Remote compute
Verify correctness or inspect measured performance	Testing and benchmarks
Change the code or documentation	Contribution workflow
Reproduce a dated experiment	Historical records

Each operation has one scientific owner: its public coordinates, units, values, and errors remain consistent while backends specialize allocation and kernels. Applications own scheduling, presentation, and explicit resource policies.

Before contributing

Read the source layout and the writing conventions. Keep Python workflows short; link advanced options to their API owner. Native products and codec layouts belong here, not in a scientist’s first example.

Benchmark claims need their exact revision, device, input, timing boundary, precision, memory, and parity evidence. Use the benchmark methodology and test guide before changing a measured path. Historical numbers retain their original qualification; a documentation reorganization does not establish new hardware performance.