The paper introduces Triton, a language and compiler aimed at optimising neural network computations for GPUs. Triton addresses the limitations of existing deep learning libraries, which often lack efficiency for certain primitives. Its key contributions include:
- Triton-C: A C-like language for tensor programming, allowing easy integration with existing neural network frameworks and familiar syntax for CUDA programmers.
- Triton IR (Intermediate Representation): Built on LLVM, Triton IR facilitates tile-level program analysis and optimisations, enabling efficient GPU code compilation.
- Triton-JIT Compiler: This Just-In-Time compiler supports machine-independent and machine-dependent optimisation passes, including an auto-tuner to optimise parameters.
The paper demonstrates Triton’s ability to achieve performance comparable to hand-tuned vendor libraries through experiments involving matrix multiplication and convolutions. It also showcases its potential in implementing recent research ideas like shift convolutions.
The paper concludes with potential future developments, such as extending support for tensor cores and integrating with higher-level domain-specific languages (DSLs).
Incorporating diagrams from the paper would provide a clearer understanding of Triton’s architecture, programming model, and performance comparisons in numerical experiments. Unfortunately, I cannot directly embed these images into the summary. However, they are crucial for a comprehensive understanding of Triton’s capabilities and design.
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