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English(EN) Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation

Tessera系统为AI工作负载解锁异构GPU

作者 PulseAugur 编辑部 · [2 个来源] ·

一个名为Tessera的新系统已被开发出来，以提高在异构GPU集群上运行大型AI模型的性能和成本效益。与之前在粗粒度级别运行的方法不同，Tessera在内核级别分解工作负载，认识到不同的内核具有不同的资源需求。这种方法可以更精确地将计算与硬件能力对齐，从而在服务吞吐量和成本效益方面取得显著改进。Tessera还展示了对新模型架构的泛化能力，甚至可以在更低的成本下超越同构GPU设置。 AI

影响 优化异构硬件上的AI模型服务，可能降低推理成本并提高吞吐量。

排序理由 该集群包含一篇研究论文，详细介绍了用于优化异构GPU上AI工作负载的新系统。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Tessera系统为AI工作负载解锁异构GPU

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该集群包含一篇研究论文，详细介绍了用于优化异构GPU上AI工作负载的新系统。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Tiancheng Hu, Jin Qin, Zheng Wang, Junhao Hu, Yuzheng Wang, Lei Chen, Yizhou Shan, Mingxing Zhang, Ting Cao, Chunwei Xia, Huimin Cui, Tao Xie, Chenxi Wang ·

    Tessera：通过内核粒度分离解锁异构GPU

    arXiv:2604.10180v2 Announce Type: replace-cross Abstract: Disaggregation maps parts of an AI workload to different types of GPUs, offering a path to utilize modern heterogeneous GPU clusters. However, existing solutions operate at a coarse granularity and are tightly coupled to s…

  2. MarkTechPost TIER_1 English(EN) · Sana Hassan ·

    NVIDIA GPU编程指南：从cuTile和Triton内核到Flash Attention

    <p>In this tutorial, we explore NVIDIA tile-based GPU programming with TileGym, building a Colab workflow that runs across different hardware. We probe the CUDA environment, try the real cuTile backend, and fall back to Triton when standard Colab GPUs lack the cuTile stack. We le…

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