index.rst



Components
HPVM consists of a few relatively independent key components.

Patched LLVM: provides HPVM IR and a compilation infrastructure, including clang and opt.
HPVM code generator: a few opt passes that lowers HPVM IR to LLVM IR,
which is then compiled into object code and binary.

:doc:`Compilation process of HPVM </specifications/hpvm-spec>`
shows how these 2 components work together.
In addition, there are:

Frontends (Keras/PyTorch): code generators in Python for lowering Keras and PyTorch
DNN models into HPVM-C format.
Predictive tuner: an autotuner library in Python for finding approximation choices (configurations)
with best performance gain within some loss of Quality of Service (QoS, such as accuracy).
HPVM profiler: an API in Python for measuring real performance of configurations.
Tensor runtime: a backend which holds implementations for some common tensor operators
(such as convolution) that HPVM-C functions can be converted into.

The interaction between these components is illustrated in this figure:

The documentation of these components are listed below,
which explains their role, usage, and other details.

Tests and Benchmarks
HPVM provides a number of general benchmarks (written in HPVM-C), DNN benchmarks,
and test cases testing the components listed above.
See this for more details on these tests and benchmarks: