What is HIP?

What is HIP?#

The Heterogeneous-computing Interface for Portability (HIP) API is a C++ runtime API and kernel language that lets developers create portable applications running in heterogeneous systems, using CPUs and AMD GPUs or NVIDIA GPUs from a single source code. HIP provides a simple marshalling language to access either the AMD ROCM back-end, or NVIDIA CUDA back-end, to build and run application kernels.

HIP in an application.

  • HIP is a thin API with little or no performance impact over coding directly in NVIDIA CUDA or AMD ROCm.

  • HIP enables coding in a single-source C++ programming language including features such as templates, C++11 lambdas, classes, namespaces, and more.

  • Developers can specialize for the platform (CUDA or ROCm) to tune for performance or handle tricky cases.

ROCm offers compilers (clang, hipcc), code profilers (rocprof, omnitrace), debugging tools (rocgdb), libraries and HIP with the runtime API and kernel language, to create heterogeneous applications running on both CPUs and GPUs. ROCm provides marshalling libraries like hipFFT or hipBLAS that act as a thin programming layer over either NVIDIA CUDA or AMD ROCm to enable support for either back-end. These libraries offer pointer-based memory interfaces and are easily integrated into your applications.

HIP supports the ability to build and run on either AMD GPUs or NVIDIA GPUs. GPU Programmers familiar with NVIDIA CUDA or OpenCL will find the HIP API familiar and easy to use. Developers no longer need to choose between AMD or NVIDIA GPUs. You can quickly port your application to run on the available hardware while maintaining a single codebase. The HIPify tools, based on the clang front-end and Perl language, can convert CUDA API calls into the corresponding HIP API calls. However, HIP is not intended to be a drop-in replacement for CUDA, and developers should expect to do some manual coding and performance tuning work for AMD GPUs to port existing projects as described HIP porting guide.

HIP provides two components: those that run on the CPU, also known as host system, and those that run on GPUs, also referred to as device. The host-based code is used to create device buffers, move data between the host application and a device, launch the device code (also known as kernel), manage streams and events, and perform synchronization. The kernel language provides a way to develop massively parallel programs that run on GPUs, and provides access to GPU specific hardware capabilities.

In summary, HIP simplifies cross-platform development, maintains performance, and provides a familiar C++ experience for GPU programming that runs seamlessly on both AMD and NVIDIA GPUs.

HIP components#

HIP consists of the following components. For information on the license associated with each component, see HIP licensing.

C++ runtime API#

For the AMD ROCm platform, HIP provides headers and a runtime library built on top of HIP-Clang compiler in the repository Compute Language Runtime (CLR). The HIP runtime implements HIP streams, events, and memory APIs, and is an object library that is linked with the application. The source code for all headers and the library implementation is available on GitHub.

For the NVIDIA CUDA platform, HIP provides headers that translate from the HIP runtime API to the CUDA runtime API. The host-side contains mostly inlined wrappers or even just preprocessor defines, with no additional overhead. The device-side code is compiled with nvcc, just like normal CUDA kernels, and therefore one can expect the same performance as if directly coding in CUDA. The CUDA specific headers can be found in the hipother repository.

For further details, check HIP Runtime API Reference.

Kernel language#

HIP provides a C++ syntax that is suitable for compiling most code that commonly appears in compute kernels (classes, namespaces, operator overloading, and templates). HIP also defines other language features that are designed to target accelerators, such as:

  • Short-vector headers that can serve on a host or device

  • Math functions that resemble those in math.h, which is included with standard C++ compilers

  • Built-in functions for accessing specific GPU hardware capabilities

For further details, check C++ language extensions and C++ language support.