Thesis Open Access

Adding CUDA® Support to Cling: JIT Compile to GPUs

Simeon Ehrig; Axel Huebl; Axel Naumann; Vassil Vassilev

Jupyter Notebooks are omnipresent in the modern scientist's and engineer's toolbox just as CUDA C++ is in accelerated computing. We present the first implementation of a CUDA C++ enabled read-eval-print-loop (REPL) that allows to interactively "script" the popular CUDA C++ runtime syntax in Notebooks. With our novel implementation, based on LLVM, Clang and CERN's C++ interpreter Cling, the modern CUDA C++ developer can work as interactively and productively as (I)Python developers while keeping all the benefits of the vast C++ computing and library ecosystem coupled with first-class performance.

Knowledge in developing applications with GPU acceleration is recommended for the talk.

Files (230.3 MB)
Name Size
Cling-CUDA_SimeonEhrig_Final.mp4
md5:66e498df0c18c5dfe55ce6d26098371c
228.9 MB Download
jupyter_notebook_demo.pdf
md5:91fff7803a4b1ad56c6ff26cb50af6fd
89.0 kB Download
llvm_develop_meeting_2020_Simeon_Ehrig_Cling-CUDA.pdf
md5:ebf5c7a7be77d3544cca535051aeab46
576.0 kB Download
presentation_with_jupyter_slides.pdf
md5:493518fe653c1fec9b4af87c7a455224
661.5 kB Download
126
91
views
downloads
All versions This version
Views 126126
Downloads 9191
Data volume 1.6 GB1.6 GB
Unique views 123123
Unique downloads 6969

Share

Cite as