Iris: First-Class Multi-GPU Programming Experience in Triton

• URL: http://arxiv.org/abs/2511.12500v1
• Date: Sun, 16 Nov 2025 08:24:45 GMT
• Title: Iris: First-Class Multi-GPU Programming Experience in Triton
• Authors: Muhammad Awad, Muhammad Osama, Brandon Potter,
• Abstract summary: We present Iris, a multi-GPU communication library implemented entirely in Python and Triton.<n>Iris provides tile-based symmetric memory abstractions that naturally align with Triton's programming model.<n>We demonstrate that Iris achieves near-optimal bandwidth utilization in microbenchmarks and delivers up to 1.79x speedup over PyTorch and RCCL.
• Score: 0.09290947230642188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-GPU programming traditionally requires developers to navigate complex trade-offs between performance and programmability. High-performance implementations typically rely on low-level HIP/CUDA communication libraries that demand substantial engineering effort for even basic overlap patterns, while simpler abstractions often sacrifice performance. We present Iris, a multi-GPU communication library implemented entirely in Python and Triton that eliminates this trade-off. Iris provides tile-based symmetric memory abstractions that naturally align with Triton's programming model, enabling developers to write single-source kernels that seamlessly interleave computation and communication. We demonstrate a taxonomy of compute-communication overlap patterns--from bulk-synchronous to fine-grained workgroup specialization--that can be implemented with minimal code changes in Iris, often requiring just a few additional lines within the same Triton kernel. Our evaluation shows that Iris achieves near-optimal bandwidth utilization in microbenchmarks and delivers up to 1.79x speedup over PyTorch and RCCL for GEMM+All-Scatter workloads, demonstrating that high-level implementations can match or exceed heavily-optimized libraries while dramatically simplifying multi-GPU programming.

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