Ben Sander
2ab19ca505
- Complete translation tables for cudaError <-> hipError_t.
- Remove some odd errors that were not correctly translated or not used.
- Add HIPCHECK_API to test infrastructure. Used for negative testing
an API ; if a mismatch occurs it shows the expected return error
code. Can also print a warning rather than error.
- Enable hipMemoryAllocate on NV system, and review error coded.
- Add hipErrorName to nvcc.
Change-Id: I680427dcf32a5796d5913cf9e7f3b4c6f6b91599
Conflicts:
tests/src/CMakeLists.txt
Bug fixes and improved docs for hipFree and hipHostFree.
- Passing NULL pointer initialized runtime and return hipSuccess
(not an error like before).
- add negative test for this. (hipMemoryAllocate, improved)
- Match NVCC errors for invalid pointers, add to test.
- Update hipFree and hipHostFree docs.
- hipGetDevicePointer always set *devicePointer=NULL, even for
invalid flags.
- Gate shared memory usage on specific HCC work-week.
Change-Id: I533b4fd3280a3d6cdbf05eb768976f0c7506c012
What is this repository for?
HIP allows developers to convert CUDA code to portable C++. The same source code can be compiled to run on NVIDIA or AMD GPUs. Key features include:
- HIP is very thin and has little or no performance impact over coding directly in CUDA or hcc "HC" mode.
- HIP allows coding in a single-source C++ programming language including features such as templates, C++11 lambdas, classes, namespaces, and more.
- HIP allows developers to use the "best" development environment and tools on each target platform.
- The "hipify" tool automatically converts source from CUDA to HIP.
- Developers can specialize for the platform (CUDA or hcc) to tune for performance or handle tricky cases
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.
More Info:
- Installation and clang-hipify
- HIP FAQ
- HIP Kernel Language
- HIP Runtime API (Doxygen)
- HIP Porting Guide
- HIP Terminology (including Rosetta Stone of GPU computing terms across CUDA/HIP/HC/AMP/OpenL)
- Developer/CONTRIBUTING Info
- Release Notes
How do I get set up?
See the Installation notes.
Examples and Getting Started:
- A sample and blog that uses hipify to convert a simple app from CUDA to HIP:
cd samples/01_Intro/square
# follow README / blog steps to hipify the application.
- A sample and blog demonstrating platform specialization:
cd samples/01_Intro/bit_extract
make
- Guide to Porting a New Cuda Project
More Examples
The GitHub repot HIP-Examples contains a hipified vesion of the popular Rodinia benchmark suite. The README with the procedures and tips the team used during this porting effort is here: Rodinia Porting Guide
Tour of the HIP Directories
-
include:
- hip_runtime_api.h : Defines HIP runtime APIs and can be compiled with many standard Linux compilers (hcc, GCC, ICC, CLANG, etc), in either C or C++ mode.
- hip_runtime.h : Includes everything in hip_runtime_api.h PLUS hipLaunchKernel and syntax for writing device kernels and device functions. hip_runtime.h can only be compiled with hcc.
- hcc_detail/** , nvcc_detail/** : Implementation details for specific platforms. HIP applications should not include these files directly.
- hcc.h : Includes interop APIs for HIP and HCC
-
bin: Tools and scripts to help with hip porting
- hipify : Tool to convert CUDA code to portable CPP. Converts CUDA APIs and kernel builtins.
- hipcc : Compiler driver that can be used to replace nvcc in existing CUDA code. hipcc ill call nvcc or hcc depending on platform, and include appropriate platform-specific headers and libraries.
- hipconfig : Print HIP configuration (HIP_PATH, HIP_PLATFORM, CXX config flags, etc)
- hipexamine.sh : Script to scan directory, find all code, and report statistics on how much can be ported with HIP (and identify likely features not yet supported)
-
doc: Documentation - markdown and doxygen info
Reporting an issue
Use the [GitHub issue tracker] (https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/issues). If reporting a bug, include the output of "hipconfig --full" and samples/1_hipInfo/hipInfo (if possible).