Frequently asked questions

Frequently asked questions#

This topic provides answers to frequently asked questions from new HIP users and users familiar with NVIDIA CUDA.

HIP Support#

What hardware does HIP support?#

HIP supports AMD and NVIDIA GPUs. See prerequisites of the install guide for detailed information.

What operating systems does HIP support?#

Linux as well as Windows are supported by ROCm. The exact versions are listed in the system requirements for Supported operating systems and Supported SKUs.

Note

Not all HIP runtime API functions are yet supported on Windows. A note is added to those functions’ documentation in the HIP runtime API reference.

What libraries does HIP provide?#

HIP provides key math and AI libraries. See ROCm libraries for the full list.

What NVIDIA CUDA features does HIP support?#

The NVIDIA CUDA runtime API supported by HIP and NVIDIA CUDA driver API supported by HIP pages describe which NVIDIA CUDA APIs are supported and what the equivalents are. The HIP API documentation describes each API and its limitations, if any, compared with the equivalent CUDA API.

The kernel language features are documented in the C++ language extensions page.

Relation to other GPGPU frameworks#

Is HIP a drop-in replacement for CUDA?#

The HIPIFY tools can automatically convert almost all CUDA runtime code to HIP. Most device code needs no additional conversion because HIP and CUDA have the same signatures for math and built-in functions except for the name. HIP code provides similar performance as native CUDA code on NVIDIA platforms, plus the benefits of being compilable for AMD platforms.

Additional porting might be required to deal with architecture feature queries or CUDA capabilities that HIP doesn’t support.

How does HIP compare with OpenCL?#

HIP offers several benefits over OpenCL:

Device code can be written in modern C++, including templates, lambdas, classes and so on.
Host and device code can be mixed in the source files.
The HIP API is less verbose than OpenCL and is familiar to CUDA developers.
Porting from CUDA to HIP is significantly easier than from CUDA to OpenCL.
HIP uses development tools specialized for each platform: amdclang++ for AMD GPUs or nvcc for NVIDIA GPUs, and profilers like ROCm Compute Profiler or Nsight Systems.
HIP provides * pointers and host-side pointer arithmetic. * device-level control over memory allocation and placement. * an offline compilation model.

How does porting CUDA to HIP compare to porting CUDA to OpenCL?#

OpenCL differs from HIP and CUDA when considering the host runtime, but even more so when considering the kernel code. The HIP device code is a C++ dialect, while OpenCL is C99-based. OpenCL does not support single-source compilation.

As a result, the OpenCL syntax differs significantly from HIP, and porting tools must perform complex transformations, especially regarding templates or other C++ features in kernels.

To better understand the syntax differences, see here or the HIP porting guide.

Can I install CUDA and ROCm on the same machine?#

Yes, but you require a compatible GPU to run the compiled code.

On NVIDIA platforms, can I mix HIP code with CUDA code?#

Yes. Most HIP types and data structures are typedef s to CUDA equivalents and can be used interchangeably. This can be useful for iteratively porting CUDA code.

See HIP porting guide for more details.

Can a HIP binary run on both AMD and NVIDIA platforms?#

HIP is a source-portable language that can be compiled to run on AMD or NVIDIA platforms. However, the HIP tools don’t create a “fat binary” that can run on both platforms.

Compiler related questions#

hipcc detected my platform incorrectly. What should I do?#

The environment variable HIP_PLATFORM can be used to specify the platform for which the code is going to be compiled with hipcc. See the hipcc environment variables for more information.

Warning

If you specify HIP_PLATFORM=NVIDIA with hipcc, you also need to pass -x cu to hipcc when compiling files with the .hip file extension. Otherwise, nvcc will not recognize the .hip file extension and will fail with nvcc fatal : Don't know what to do with <file>.hip.

How to use HIP-Clang to build HIP programs?#

hipcc is a compiler driver. This means it is not a compiler but calls the appropriate compilers and sets some options.

The underlying compilers are amdclang++ or nvcc, depending on the platform, and can be called directly.

What is HIP-Clang?#

HIP-Clang is a Clang/LLVM-based compiler used to compile HIP programs for AMD platforms. The executable is named amdclang++ on Linux and clang++ on Windows.

Can I link HIP device code with host code compiled with another compiler such as gcc, icc, or clang?#

Yes. HIP generates object code that conforms to the GCC ABI, and links with libstdc++. This means you can compile host code with the compiler of your choice and link the generated host object code with device code.

Can HIP applications be compiled with a C compiler?#

HIP is a C/C++ API that can be used with C compilers. However, this applies only to the API itself. Device code and the syntax for calling kernels must be compiled with a supported compiler like hipcc. The code objects that are generated with hipcc can, however, be used with a C compiler, as shown in the code examples below.

The following is the HIP device code, assumed to be saved in device.hip:

#include <hip/hip_runtime.h>

__global__ void kernel(double* array, size_t size){
    const int x = threadIdx.x + blockIdx.x * blockDim.x;
    if(x < size){array[x] = x;}
};

extern "C"{
    hipError_t callKernel(int blocks, int threadsPerBlock, double* array, size_t size){
        kernel<<<blocks, threadsPerBlock, 0, hipStreamDefault>>>(array, size);
        return hipGetLastError();
    }
}

The following is the host code, written in C, saved in host.c:

#include <hip/hip_runtime_api.h>
#include <stdio.h>
#include <stdlib.h>

#define HIP_CHECK(c) {                                \
   if (c != hipSuccess){                              \
      printf("HIP Error : %s", hipGetErrorString(c)); \
      printf(" %s %d\n", __FILE__, __LINE__);         \
      exit(c);                                        \
   }                                                  \
}

// Forward declaration - the implementation needs to be compiled with
// a device compiler like hipcc or amdclang++
hipError_t callKernel(int blocks, int threadsPerBlock, double* array, size_t size);

int main(int argc, char** argv) {
    int blocks = 1024;
    int threadsPerBlock = 256;
    size_t arraySize = blocks * threadsPerBlock;
    double* d_array;
    double* h_array;
    h_array = (double*)malloc(arraySize * sizeof(double));

    HIP_CHECK(hipMalloc((void**)&d_array, arraySize * sizeof(double)));
    HIP_CHECK(callKernel(blocks, threadsPerBlock, d_array, arraySize));
    HIP_CHECK(hipMemcpy(h_array, d_array, arraySize * sizeof(double), hipMemcpyDeviceToHost));
    HIP_CHECK(hipFree(d_array));

    free(h_array);
    return 0;
}

These files are then compiled and linked using

hipcc -c device.hip
gcc host.c device.o $(hipconfig --cpp_config) -L/opt/rocm/lib -lamdhip64

assuming the default installation of ROCm in /opt/rocm.

How to guard code specific to the host or the GPU?#

The compiler defines the __HIP_DEVICE_COMPILE__ macro only when compiling device code.

Refer to the HIP porting guide for more information.