HIP provides the following:
Devices (hipSetDevice(), hipGetDeviceProperties())
Memory management (hipMalloc(), hipMemcpy(), hipFree())
Streams (hipStreamCreate(),hipStreamSynchronize(), hipStreamWaitEvent())
Events (hipEventRecord(), hipEventElapsedTime())
Kernel launching (hipLaunchKernel is a standard C/C++ function that replaces <<< >>>)
HIP Module API to control when adn how code is loaded.
CUDA-style kernel coordinate functions (threadIdx, blockIdx, blockDim, gridDim)
Cross-lane instructions including shfl, ballot, any, all - Most device-side math built-ins.
Error reporting (hipGetLastError(), hipGetErrorString())
The HIP API documentation describes each API and its limitations, if any, compared with the equivalent CUDA API.
At a high-level, the following features are not supported:
Textures (partial support available)
Dynamic parallelism (CUDA 5.0)
Managed memory (CUDA 6.5)
Graphics interoperability with OpenGL or Direct3D
CUDA IPC Functions (Under Development)
CUDA array, mipmappedArray and pitched memory
Queue priority controls
See the API Support Table for more detailed information.
C++-style device-side dynamic memory allocations (free, new, delete) (CUDA 4.0)
Virtual functions, indirect functions and try/catch (CUDA 4.0)
PTX assembly (CUDA 4.0). HIP-Clang supports inline GCN assembly..
Several kernel features are under development. See the HIP Kernel Language for more information. This includes:
No. HIP provides porting tools which do most of the work to convert CUDA code into portable C++ code that uses the HIP APIs. Most developers will port their code from CUDA to HIP and then maintain the HIP version. HIP code provides the same performance as native CUDA code, plus the benefits of running on AMD platforms.
HIP APIs and features do not map to a specific CUDA version. HIP provides a strong subset of the functionality provided in CUDA, and the hipify tools can scan code to identify any unsupported CUDA functions. This is useful for identifying the specific features required by a given application.
However, we can provide a rough summary of the features included in each CUDA SDK and the support level in HIP. Each bullet below lists the major new language features in each CUDA release and then indicate which are supported/not supported in HIP:
CUDA 4.0 and earlier :
HIP supports CUDA 4.0 except for the limitations described above.
CUDA 5.0 :
Dynamic Parallelism (not supported)
cuIpc functions (under development).
CUDA 5.5 :
CUPTI (not directly supported, AMD GPUPerfAPI can be used as an alternative in some cases)
CUDA 6.0 :
Managed memory (under development)
CUDA 6.5 :
__shfl intriniscs (supported)
CUDA 7.0 :
Per-thread-streams (under development)
C++11 (Hip-Clang supports all of C++11, all of C++14 and some C++17 features)
CUDA 7.5 :
CUDA 8.0 :
Page Migration including cudaMemAdvise, cudaMemPrefetch, other cudaMem* APIs(not supported)
CUDA 9.0 :
Cooperative Launch, Surface Object Management, Version Management
HIP includes growing support for the four key math libraries using hcBlas, hcFft, hcrng and hcsparse, as well as MIOpen for machine intelligence applications. These offer pointer-based memory interfaces (as opposed to opaque buffers) and can be easily interfaced with other HIP applications. The hip interfaces support both ROCm and CUDA paths, with familiar library interfaces.
Additionally, some of the cublas routines are automatically converted to hipblas equivalents by the HIPIFY tools. These APIs use cublas or hcblas depending on the platform and replace the need to use conditional compilation.
Both AMD and Nvidia support OpenCL 1.2 on their devices so that developers can write portable code. HIP offers several benefits over OpenCL:
Developers can code in C++ as well as mix host and device C++ code in their source files. HIP C++ code can use templates, lambdas, classes and so on.
The HIP API is less verbose than OpenCL and is familiar to CUDA developers.
Because both CUDA and HIP are C++ languages, porting from CUDA to HIP is significantly easier than porting from CUDA to OpenCL.
HIP uses the best available development tools on each platform: on Nvidia GPUs, HIP code compiles using NVCC and can
employ the nSight profiler and debugger (unlike OpenCL on Nvidia GPUs).
HIP provides pointers and host-side pointer arithmetic.
HIP provides device-level control over memory allocation and placement.
HIP offers an offline compilation model.
Both HIP and CUDA are dialects of C++, and thus porting between them is relatively straightforward. Both dialects support templates, classes, lambdas, and other C++ constructs. As one example, the hipify-perl tool was originally a Perl script that used simple text conversions from CUDA to HIP. HIP and CUDA provide similar math library calls as well. In summary, the HIP philosophy was to make the HIP language close enough to CUDA that the porting effort is relatively simple. This reduces the potential for error, and also makes it easy to automate the translation. HIP’s goal is to quickly get the ported program running on both platforms with little manual intervention, so that the programmer can focus on performance optimizations.
There have been several tools that have attempted to convert CUDA into OpenCL, such as CU2CL. OpenCL is a C99-based kernel language (rather than C++) and also does not support single-source compilation. As a result, the OpenCL syntax is different from CUDA, and the porting tools have to perform some heroic transformations to bridge this gap. The tools also struggle with more complex CUDA applications, in particular, those that use templates, classes, or other C++ features inside the kernel.
For a list of AMD-supported platforms, refer to the HIP Programming Guide.
For Nvidia platforms, HIP requires Unified Memory and should run on any device supporting CUDA SDK 6.0 or newer. We have tested the NVIDIA Titan and Tesla K40.
Typically, HIPIFY tools can automatically convert almost all run-time code, and the coordinate indexing device code ( threadIdx.x -> hipThreadIdx_x ). Most device code needs no additional conversion since HIP and CUDA have similar names for math and built-in functions. The hipify-clang tool will automatically modify the kernel signature as needed (automating a step that used to be done manually). Additional porting may be required to deal with architecture feature queries or with CUDA capabilities that HIP doesn’t support. In general, developers should always expect to perform some platform-specific tuning and optimization.
NVCC is Nvidia’s compiler driver for compiling CUDA C++ code into PTX or device code for Nvidia GPUs. It’s a closed-source binary compiler that is provided by the CUDA SDK.
HIP-Clang is a Clang/LLVM based compiler to compile HIP programs, which can run on the AMD platform.
While HIP is a strong subset of the CUDA, it is a subset. The HIP layer allows that subset to be clearly defined and documented. Developers who code to the HIP API can be assured their code will remain portable across Nvidia and AMD platforms. In addition, HIP defines portable mechanisms to query architectural features and supports a larger 64-bit wavesize which expands the return type for cross-lane functions like ballot and shuffle from 32-bit ints to 64-bit ints.
Yes. HIP’s CUDA path only exposes the APIs and functionality that work on both NVCC and AMDGPU back-ends. APIs, parameters, and features which exist in CUDA but not in HIP-Clang will typically result in compile-time or run-time errors. Developers need to use the HIP API for most accelerator code and bracket any CUDA-specific code with preprocessor conditionals. Developers concerned about portability should, of course, run on both platforms, and should expect to tune for performance. In some cases, CUDA has a richer set of modes for some APIs, and some C++ capabilities such as virtual functions - see the HIP @API documentation for more details.
Yes. HIP-Clang path only exposes the APIs and functions that work on AMD runtime back ends. APIs, parameters, and features that appear in HIP-Clang but not CUDA will typically cause compile or run-time errors. Developers must use the HIP API for most accelerator code and bracket any HIP-Clang specific code with preprocessor conditionals. Those concerned about portability should, of course, test their code on both platforms and should tune it for performance.
Typically, HIP-Clang supports a more modern set of C++11/C++14/C++17 features, so HIP developers who want portability should be careful when using advanced C++ features on the HIP-Clang path.
The environment variable can be used to set compiler path:
HIP_CLANG_PATH: path to hip-clang. When set, this variable let hipcc to use hip-clang for compilation/linking.
There is an alternative environment variable to set compiler path:
HIP_ROCCLR_HOME: path to root directory of the HIP-ROCclr runtime. When set, this variable let hipcc use hip-clang from the ROCclr distribution.
NOTE: If HIP_ROCCLR_HOME is set, there is no need to set HIP_CLANG_PATH since hipcc will deduce them from HIP_ROCCLR_HOME.
ROCclr (Radeon Open Compute Common Language Runtime) is a virtual device interface that compute runtimes interact with backends such as ROCr on Linux, as well as PAL on Windows.
HIP is a source-portable language that can be compiled to run on either AMD or NVIDIA platform. HIP tools don’t create a fat binary that can run on either platform.
Yes. You can use HIP_PLATFORM to choose which path hipcc targets. This configuration can be useful when using HIP to develop an application which is portable to both AMD and NVIDIA.
HIP sets the platform to AMD and use HIP-Clang as the compiler if the AMD graphics driver is installed and has detected an AMD GPU.
If this is not what you want, you can force HIP to recognize the platform by setting the following,
HIP then sets and uses the correct AMD compiler and runtime:
To choose the NVIDIA platform, you can set,
In this case, HIP will set and use the following,
A symptom of this problem is the error message:
‘an unknown error(11) at square.hipref.cpp:56’
This error can occur if you have a CUDA installation on an AMD platform, and HIP incorrectly detects the platform as nvcc. HIP may be able to compile the application using the nvcc tool-chain, however, it will generate this error at runtime as the platform does not have a CUDA device.
Yes. Most HIP data structures (hipStream_t, hipEvent_t) are typedefs to CUDA equivalents and can be intermixed. Both CUDA and HIP use integer device ids. One notable exception is that hipError_t is a new type, and cannot be used where a cudaError_t is expected. In these cases, refactor the code to remove the expectation. Alternatively, hip_runtime_api.h defines functions which convert between the error code spaces:
hipErrorToCudaError hipCUDAErrorTohipError hipCUResultTohipError
If platform portability is important, use #ifdef HIP_PLATFORM_NVCC to guard the CUDA-specific code.
Product of block.x, block.y, and block.z should be less than 1024.
__shfl_*_sync is not supported on HIP but for NVCC path CUDA 9.0. Above all, shuffle calls get redirected to its sync version.
The compiler defines the __HIP_DEVICE_COMPILE__ macro only when compiling the code for the GPU. It could be used to guard code that is specific to the host or the GPU.
When compiling an OpenMP source file with hipcc -fopenmp, the compiler may generate an error if there is a reference to the _OPENMP macro. This is due to a limitation in hipcc that treats any source file type (e.g., .cpp) as HIP translation unit leading to some conflicts with the OpenMP language switch. If the OpenMP source file doesn’t contain any HIP language construct, you could work around this issue by adding the -x c++ switch to force the compiler to treat the file as regular C++.
Another approach would be to guard the OpenMP code with #ifdef _OPENMP so that the code block is disabled when compiling for the GPU. The __HIP_DEVICE_COMPILE__ macro defined by the HIP compiler when compiling the GPU code could also be used for guarding code paths specific to the host or the GPU.