HIP: Heterogenous-computing Interface for Portability
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HIP provides the following:
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:
See the API Support Table for more detailed information.
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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:
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:
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.
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.
HCC is AMD's compiler driver which compiles "heterogeneous C++" code into HSAIL or GCN device code for AMD GPUs. It's an open-source compiler based on recent versions of CLANG/LLVM.
In ROCM v3.5 release, HCC compiler is deprecated and HIP-Clang compiler is introduced to compile HIP programs.
HIP-Clang is new compiler to emphasize its capability to compile HIP programs which can run on 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. "Extra" 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's HIP-Clang path only exposes the APIs and functions that work on AMD runtime back ends. "Extra" 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. In ROCM v3.5 release, HCC compiler is deprecated, and the HIP-Clang compiler can be used for compiling HIP programs.
For most HIP applications, the transition from HCC to HIP-Clang is transparent as the HIPCC and HIP cmake files automatically choose compiler options for HIP-Clang and hide the difference between the HCC and HIP-Clang code. However, minor changes may be required as HIP-Clang has stricter syntax and semantic checks compared to HCC.
The environment variable can be used to set compiler path:
There is an alternative environment variable to set compiler path:
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, however.
HIP is a portable C++ language that supports a strong subset of the CUDA run-time APIs and device-kernel language. It's designed to simplify CUDA conversion to portable C++. HIP provides a C-compatible run-time API, C-compatible kernel-launch mechanism, C++ kernel language and pointer-based memory management.
A C++ dialect, hc is supported by the AMD compiler. It provides C++ run time, C++ kernel-launch APIs (parallel_for_each), C++ kernel language, and several memory-management options, including pointers, arrays and array_view (with implicit data synchronization). It's intended to be a leading indicator of the ISO C++ standard. The HCC compiler has been deprecated in the ROCm Release v3.5.
Yes. HIP generates the object code which conforms to the GCC ABI, and also links with libstdc++. This means you can compile host code with the compiler of your choice and link the generated object code with GPU code compiled with HIP. Larger projects often contain a mixture of accelerator code (initially written in CUDA with nvcc) and host code (compiled with gcc, icc, or clang). These projects can convert the accelerator code to HIP, compile that code with hipcc, and link with object code from their preferred compiler.
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 will set the platform to hcc and compiler to HIP-Clang if it sees that the AMD graphics driver is installed and has detected an AMD GPU. Sometimes this isn't what you want - you can force HIP to recognize the platform by setting the following,
One symptom of this problem is the message "error: 'unknown error'(11) at square.hipref.cpp:56". This 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 but will generate this error at runtime since the platform does not have a CUDA device. The fix is to set HIP_PLATFORM=hcc and rebuild.
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.
No. HC functionality is not supported by HIP-Clang.
See the HIP Profiling Guide for more information.
Unlike CUDA, in HIP_Clang, for functions defined in the header files, the keyword of "__forceinline__" does not imply "static". Thus, if failed to define "static" keyword, you might see a lot of "symbol multiply defined!" errors at compilation. The workaround is to explicitly add the keyword of "static" before any functions that were defined as "__forceinline__".
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 and above all shuffle calls get redirected to it's sync version.