[llvm-dev] OrcJIT + CUDA Prototype for Cling

[Simeon Ehrig via llvm-dev]

Dear LLVM-Developers and Vinod Grover,

we are trying to extend the cling C++ interpreter
(https://github.com/root-project/cling) with CUDA functionality for
Nvidia GPUs.

I already developed a prototype based on OrcJIT and am seeking for
feedback. I am currently a stuck with a runtime issue, on which my
interpreter prototype fails to execute kernels with a CUDA runtime error.


=== How to use the prototype

This application interprets cuda runtime code. The program needs the
whole cuda-program (.cu-file) and its pre-compiled device code (as
fatbin) as an input:

    command: cuda-interpreter [source].cu [kernels].fatbin

I also implemented an alternative mode, which is generating an object
file. The object file can be linked (ld) to an exectuable. This mode is
just implemented to check if the LLVM module generation works as
expected. Activate it by changing the define INTERPRET from 1 to 0 .

=== Implementation

The prototype is based on the clang example in

https://github.com/llvm-mirror/clang/tree/master/examples/clang-interpreter

I also pushed the source code to github with the install instructions
and examples:
  https://github.com/SimeonEhrig/CUDA-Runtime-Interpreter

The device code generation can be performed with either clang's CUDA
frontend or NVCC to ptx.

Here is the workflow in five stages:

 1. generating ptx device code (a kind of nvidia assembler)
 2. translate ptx to sass (machine code of ptx)
 3. generate a fatbinray (a kind of wrapper for the device code)
 4. generate host code object file (use fatbinary as input)
 5. link to executable

(The exact commands are stored in the commands.txt in the github repo)

The interpreter replaces the 4th and 5th step. It interprets the host
code with pre-compiled device code as fatbinary. The fatbinary (Step 1
to 3) will be generated with the clang compiler and the nvidia tools
ptxas and fatbinary.

=== Test Cases and Issues

You find the test sources on GitHub in the directory "example_prog".

Run the tests with cuda-interpeter and the two arguments as above:

 [1] path to the source code in "example_prog"
     - note: even for host-only code, use the file-ending .cu
    
 [2] path to the runtime .fatbin
     - note: needs the file ending .fatbin
     - a fatbin file is necessary, but if the program doesn't need a
kernel, the content of the file will ignore

Note: As a prototype, the input is just static and barely checked yet.

1. hello.cu: simple c++ hello world program with cmath library call
sqrt() -> works without problems

2. pthread_test.cu: c++ program, which starts a second thread -> works
without problems

3. fat_memory.cu: use cuda library and allocate about 191 MB of VRAM.
After the allocation, the program waits for 3 seconds, so you can check
the memory usage with the nvidia-smi -> works without problems

4. runtime.cu: combine cuda library with a simple cuda kernel ->
Generating an object file, which can be linked (see 5th call in commands
above -> ld ...) to a working executable.

The last example has the following issues: Running the executable works
fine. Interpreting the code instead does not work. The Cuda Runtime
returns the error 8 (cudaErrorInvalidDeviceFunction) , the kernel failed.

Do you have any idea how to proceed?


Best regards,
Simeon Ehrig
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