HIP is a C++ Runtime API and Kernel Language that allows developers to create portable applications for AMD and NVIDIA GPUs from single source code.
Key features include:
New projects can be developed directly in the portable HIP C++ language and can run on either NVIDIA or AMD platforms. Additionally, HIP provides porting tools which make it easy to port existing CUDA codes to the HIP layer, with no loss of performance as compared to the original CUDA application. 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 to complete the port.
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The HIP repository maintains several branches. The branches that are of importance are:
HIP releases are typically naming convention for each ROCM release to help differentiate them.
See the Installation notes.
The HIP API includes functions such as hipMalloc, hipMemcpy, and hipFree. Programmers familiar with CUDA will also be able to quickly learn and start coding with the HIP API. Compute kernels are launched with the "hipLaunchKernelGGL" macro call. Here is simple example showing a snippet of HIP API code:
hipMalloc(&A_d, Nbytes);
hipMalloc(&C_d, Nbytes);
hipMemcpy(A_d, A_h, Nbytes, hipMemcpyHostToDevice);
const unsigned blocks = 512;
const unsigned threadsPerBlock = 256;
hipLaunchKernelGGL(vector_square, /* compute kernel*/
dim3(blocks), dim3(threadsPerBlock), 0/*dynamic shared*/, 0/*stream*/, /* launch config*/
C_d, A_d, N); /* arguments to the compute kernel */
hipMemcpy(C_h, C_d, Nbytes, hipMemcpyDeviceToHost);The HIP kernel language defines builtins for determining grid and block coordinates, math functions, short vectors, atomics, and timer functions. It also specifies additional defines and keywords for function types, address spaces, and optimization controls (See the HIP Kernel Language for a full description). Here's an example of defining a simple 'vector_square' kernel.
template <typename T>
__global__ void
vector_square(T *C_d, const T *A_d, size_t N)
{
size_t offset = (blockIdx.x * blockDim.x + threadIdx.x);
size_t stride = blockDim.x * gridDim.x;
for (size_t i=offset; i<N; i+=stride) {
C_d[i] = A_d[i] * A_d[i];
}
}The HIP Runtime API code and compute kernel definition can exist in the same source file - HIP takes care of generating host and device code appropriately.
HIP C++ code can be compiled with either,
Thus HIP source code can be compiled to run on either platform. Platform-specific features can be isolated to a specific platform using conditional compilation. Thus HIP provides source portability to either platform. HIP provides the hipcc compiler driver which will call the appropriate toolchain depending on the desired platform.
A sample and blog that uses any of HIPIFY tools to convert a simple app from CUDA to HIP:
cd samples/01_Intro/square
# follow README / blog steps to hipify the application.Guide to Porting a New Cuda Project
The GitHub repository HIP-Examples contains a hipified version of benchmark suite. Besides, there are more samples in Github HIP samples, showing how to program with different features, build and run.
include:
bin: Tools and scripts to help with hip porting
docs: Documentation - markdown and doxygen info.
Use the GitHub issue tracker. If reporting a bug, include the output of "hipconfig --full" and samples/1_hipInfo/hipInfo (if possible).