- Print issues with in categories: error, warn, info messages.
- Trace additional events and sources of deadlocks and other issues.
- Allow compilation with GNU even if "omp-tools.h" is not present.
- Allow singular translation units under BSD-3 license.
- Print more accurate location of warning.
CP2K_OMP_TRACE=1: assert errors at the place/location of the issue (nice debug experience)
CP2K_OMP_TRACE=2: print error issues (printing the code location needs debug symbols)
CP2K_OMP_TRACE=3: print warnings or info messages (when CP2K terminates)
Printing instead of asserting issues (errors, warnings, info) benefits from debug symbols. A negative value of CP2K_OMP_TRACE represents maximum verbosity.
* There are three main implementations
1. DBM_MULTIPLY_SPLIT=1: default kernel with smallest LOC-number uses private/register-backed buffer. This implementation is simple and performs best even on different vendor's GPUs. There is moderate unrolling/code-bloat leveraging macros. Though, there is no "old school GPU" like copy-into SLM; if there is re-use, it leverage existing caches between registers and global memory.
2. DBM_MULTIPLY_SPLIT=2..N with DBM_MULTIPLY_SPLIT=8 matching the CUDA implementation using shared/local memory. This implementation matches the existing CUDA implementation and perform equal and reasonable across GPUs. There is moderate unrolling/code-bloat leveraging macros; heuristics are revised/different from CUDA implementation (less cases).
3. DBM_MULTIPLY_GEN=1: uses https://github.com/intel/tiny-tensor-compiler and IR describing DBM's input format. The IR-file (https://github.com/hfp/cp2k/blob/master/src/dbm/dbm_multiply_opencl.ir) is not part of PR. The generated kernel-code is not yet competitive.
* The amount of code in the OpenCL-kernel and and C based glue-code can be *significantly* reduced (fraction of LOC) when removing for instance implementation 2 and 3 (above mentioned). Reducing the number of LOC can be part of a future cleanup (and once code is settled).
* check_file_properties.py: support C-comments, and omit checking OpenCL source code (it is usually compiled at runtime, i.e., machine-driven rather than user-controlled).
* This work "short-cuts" supporting OpenCL by leveraging DBCSR's OpenCL backend to implement CP2K's Offload interface.
- Future work (may be soon) can decide/settle where the actual glue code is hosted making the "OpenCL backend".
- OpenCL is very close to CUDA (streams/queues, events, etc), however, an OpenCL backend accounts for differences.
- Pointer arithmetic on the host using device pointers is not supported with OpenCL without additional effort.
- OpenCL abstracts device memory being "cl_mem" structure and does not expose actual device pointers.
* Some functions on CP2K's Offload interface (once derived from DBCSR's ACC interface) remain "TODO".
* For the time being offload_runtime.h "disables" support for Grid and PW/FFTs
- Defines for __NO_OFFLOAD_GRID and __NO_OFFLOAD_PW must be still given for the Fortran code.
* Some changes remove CUDA and HIP specific code-paths by relying on Offload runtime (e.g., offload_create_buffer).
* Some changes also address problematic code format (multi-line control-flow w/o curly braces/block, etc).
* Further updating INSTALL.md and white-listing/removing certain items are part of follow-up changes.
* Cleaned FLAG_EXCEPTIONS (tools/precommit/check_file_properties.py).
- the initial code will fail on systems where libxsmm is not installed
because the cpp test assumes the macro LIBXSMM_VERSION4 exists. The
macro is present in global namespace only when libxsmm.h is included
Calling it with multiple filenames was broken, revised and modernized
the script in the process. The cloud-based precommit was running it on
all files even though it was never intended to work on shell scripts.
Fixed that as well.
- Add support for AMD gpus in the toolchain
- Works with both Nvidia and AMD gpus
- generic kernels with templated types
- Needs ROCM 4.2 and CUDA 11 (for compiling on nvidia hardware)
- task list creation is hardware agnostic
