Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs
📰 ArXiv cs.AI
Learn how to evaluate CUDA Tile for AI workloads on Hopper and Blackwell GPUs and improve performance with tile-centric abstraction
Action Steps
- Install CUDA Tile and cuBLAS on your system using pip and NVIDIA's official repositories
- Run benchmarking tests on Hopper and Blackwell GPUs using CuTile, cuBLAS, Triton, WMMA, and raw SIMT
- Compare performance metrics such as execution time, memory usage, and Tensor Core utilization
- Apply tile-centric abstraction to optimize AI workloads on modern GPUs
- Test and validate the results using real-world AI applications
Who Needs to Know This
AI engineers and researchers working with NVIDIA GPUs can benefit from this evaluation to optimize their AI workloads
Key Insight
💡 CUDA Tile's tile-centric abstraction can simplify GPU kernel development while retaining efficiency on modern GPUs
Share This
Boost AI performance on Hopper and Blackwell GPUs with CUDA Tile! #CUDA #AI #GPUs
Key Takeaways
Learn how to evaluate CUDA Tile for AI workloads on Hopper and Blackwell GPUs and improve performance with tile-centric abstraction
Full Article
Title: Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs
Abstract:
arXiv:2604.23466v1 Announce Type: cross Abstract: NVIDIA's CUDA Tile (CuTile) introduces a Python-based, tile-centric abstraction for GPU kernel development that aims to simplify programming while retaining Tensor Core and Tensor Memory Accelerator (TMA) efficiency on modern GPUs. We present the first independent, cross-architecture evaluation of CuTile against established approaches such as cuBLAS, Triton, WMMA, and raw SIMT on three NVIDIA GPUs spanning Hopper and Blackwell: H100 NVL, B200, an
Abstract:
arXiv:2604.23466v1 Announce Type: cross Abstract: NVIDIA's CUDA Tile (CuTile) introduces a Python-based, tile-centric abstraction for GPU kernel development that aims to simplify programming while retaining Tensor Core and Tensor Memory Accelerator (TMA) efficiency on modern GPUs. We present the first independent, cross-architecture evaluation of CuTile against established approaches such as cuBLAS, Triton, WMMA, and raw SIMT on three NVIDIA GPUs spanning Hopper and Blackwell: H100 NVL, B200, an
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