This Thursday (9am California Time, 17:00 UTC ), Tal Ben-Nun and Berke Ates will talk about their work on a data-centric dialect for MLIR.
More info from: GitHub - spcl/dace: DaCe - Data Centric Parallel Programming
DaCe is a parallel programming framework that takes code in Python/NumPy and other programming languages, and maps it to high-performance CPU, GPU, and FPGA programs, which can be optimized to achieve state-of-the-art. Internally, DaCe uses the Stateful DataFlow multiGraph (SDFG) data-centric intermediate representation : A transformable, interactive representation of code based on data movement. Since the input code and the SDFG are separate, it is possible to optimize a program without changing its source, so that it stays readable. On the other hand, transformations are customizable and user-extensible, so they can be written once and reused in many applications. With data-centric parallel programming, we enable direct knowledge transfer of performance optimization, regardless of the application or the target processor.
DaCe generates high-performance programs for:
- Multi-core CPUs (tested on Intel and IBM POWER9)
- NVIDIA GPUs
- AMD GPUs (with HIP)
- Xilinx FPGAs
- Intel FPGAs
As usual the information to join the meeting:
+1 218-301-8485 PIN: 255 745#
I’ll also update this thread with slides and recording after the meeting.