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UID:pretalx-euroscipy-2026-G3SRPL@pretalx.com
DTSTART;TZID=CET:20260720T152000
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DESCRIPTION:Parallel programming can be intimidating\, but doesn’t need t
 o be! Tile-based programming models make GPU parallelism more newcomer-fri
 endly\, highly productive\, and still fast by letting you write sequential
 \, array-centric code while the framework handles parallelization\, synchr
 onization\, and data movement.\n\nIn this example-driven talk\, we’ll in
 troduce tile-based programming in Python using NVIDIA’s new stack: [cuTi
 le](https://github.com/NVIDIA/cutile-python) and its compiler foundation\,
  [Tile IR](https://github.com/NVIDIA/cuda-tile). You’ll see recently ann
 ounced CUDA Tile capabilities in action\, including multi-GPU communicatio
 n\, interoperability with traditional CUDA SIMT\, and support for more div
 erse kernels such as convolutions and stencils. We’ll compare tile and S
 IMT approaches\, build intuition for performance and execution\, and demon
 strate practical debugging and reasoning techniques. Along the way\, you
 ’ll see real workloads: HPC stencils\, an SPMV plus CG solver\, and ML m
 odels from [TileGym](https://github.com/NVIDIA/TileGym). You’ll leave wi
 th a clear sense of when tile programming helps\, and how it enables more 
 portable high-performance Python as hardware trends evolve.
DTSTAMP:20260603T201710Z
LOCATION:Room 1.38 (Ground Floor\, Turing)
SUMMARY:Python Tile Programming for GPUs - Bryce Adelstein Lelbach
URL:https://pretalx.com/euroscipy-2026/talk/G3SRPL/
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