Tawa: Automatic Warp Specialization for Modern GPUs with Asynchronous References

• URL: http://arxiv.org/abs/2510.14719v1
• Date: Thu, 16 Oct 2025 14:20:00 GMT
• Title: Tawa: Automatic Warp Specialization for Modern GPUs with Asynchronous References
• Abstract summary: We present Tawa, an automated compiler that generates high-performance, warp-specialized code from a high-level, tile-based program.<n>Tawai automatically partitions programs into producer-consumer roles and manages the intricate dataflow pipeline, relieving developers of invasive kernel rewriting.<n>Tawai delivers high hardware utilization, achieving up to 1.1$times$ speedup over highly optimized cuBLAS GEMM kernels.
• Score: 40.60208235389293
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern GPUs feature specialized hardware units that enable high-performance, asynchronous dataflow execution. However, the conventional SIMT programming model is fundamentally misaligned with this task-parallel hardware, creating a significant programmability gap. While hardware-level warp specialization is the key to unlocking peak performance, it forces developers to manually orchestrate complex, low-level communication and software pipelines--a process that is labor-intensive, error-prone, and unsustainable. To address this challenge, we present Tawa, an automated compiler that systematically generates high-performance, warp-specialized code from a high-level, tile-based program. Central to our approach is a novel IR abstraction, asynchronous references (aref), which expresses warp-level communication without exposing low-level hardware details. Using this abstraction, Tawa automatically partitions programs into producer-consumer roles and manages the intricate dataflow pipeline, relieving developers of invasive kernel rewriting. Evaluation on NVIDIA H100 GPUs across representative LLM kernels shows that Tawa delivers high hardware utilization, achieving up to 1.1$\times$ speedup over highly optimized cuBLAS GEMM kernels. For attention workloads, Tawa attains 1.2$\times$ speedup over Triton and matches the performance of the hand-optimized CUTLASS C++ FlashAttention-3 kernel with far less programming effort.

Related papers

• Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU [38.81440160993858]
  Porting deep learning algorithms to new hardware accelerators requires developers to apply the same low-level optimizations to every Triton kernel in their code-base.<n>We present Xe-Forge, a multi-stage LLM-powered pipeline that automates this process for Intel GPU.<n>A curated knowledge base encodes Intel GPU constraints that are absent from LLM training data, keeping the model within architecturally valid bounds.
  arXiv  Detail & Related papers  (2026-04-16T20:21:01Z)
• StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning [26.264303471292845]
  We propose StitchCUDA, a multi-agent framework for end-to-end GPU program generation.<n> Experiments show that StitchCUDA achieves nearly 100% success rate on end-to-end programming tasks, with 1.72x better speedup than the multi-agent baseline.
  arXiv  Detail & Related papers  (2026-03-03T06:04:49Z)
• CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation [51.72529978689561]
  Agent is a large-scale agentic reinforcement learning system that develops kernel expertise through three components.<n>Agent delivers 100%, 100%, and 92% faster rate over torchcompile on KernelBench.
  arXiv  Detail & Related papers  (2026-02-27T18:58:05Z)
• KernelCraft: Benchmarking for Agentic Close-to-Metal Kernel Generation on Emerging Hardware [25.808580418841718]
  New AI accelerators often require developers to manually craft low-level kernels.<n>This prevents emerging hardware platforms from reaching the market efficiently.<n>We present KernelCraft: the first benchmark to evaluate an agent's ability to generate and optimize low-level kernels for customized accelerators.
  arXiv  Detail & Related papers  (2026-02-10T14:52:02Z)
• AKG kernel Agent: A Multi-Agent Framework for Cross-Platform Kernel Synthesis [13.239454996851771]
  Modern AI models demand high-performance computation kernels.<n>Akg kernel agent (AI-driven Kernel Generator) is designed to support multiple domain-specific languages.<n>System's modular design allows rapid integration of backend DSLs and hardware targets.<n>System achieves an average speedup of 1.46$times over PyTorch Eager baselines.
  arXiv  Detail & Related papers  (2025-12-29T12:42:05Z)
• Iris: First-Class Multi-GPU Programming Experience in Triton [0.09290947230642188]
  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.
  arXiv  Detail & Related papers  (2025-11-16T08:24:45Z)
• Eliminating Multi-GPU Performance Taxes: A Systems Approach to Efficient Distributed LLMs [61.953548065938385]
  We introduce the ''Three Taxes'' (Bulk Synchronous, Inter- Kernel Data Locality, and Kernel Launch Overhead) as an analytical framework.<n>We propose moving beyond the rigid BSP model to address key inefficiencies in distributed GPU execution.<n>We observe a 10-20% speedup in end-to-end latency over BSP-based approaches.
  arXiv  Detail & Related papers  (2025-11-04T01:15:44Z)
• Dato: A Task-Based Programming Model for Dataflow Accelerators [13.87015257740592]
  We present Dato, a Python-embedded, task-based programming model for dataflow accelerators.<n>Dato elevates data communication and sharding to first-class type constructs.<n>Dato achieves high performance while significantly reducing the burden of writing optimized code.
  arXiv  Detail & Related papers  (2025-09-08T15:22:51Z)
• Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition [95.54406667705999]
  Pangu Embedded is an efficient Large Language Model (LLM) reasoner developed on Ascend Neural Processing Units (NPUs)<n>It addresses the significant computational costs and inference latency challenges prevalent in existing reasoning-optimized LLMs.<n>It delivers rapid responses and state-of-the-art reasoning quality within a single, unified model architecture.
  arXiv  Detail & Related papers  (2025-05-28T14:03:02Z)
• TileLang: A Composable Tiled Programming Model for AI Systems [17.240134151647187]
  We present TileLang, a generalized tiled programming model for more efficient AI programming.<n> TileLang decouples scheduling space (thread binding, layout, tensorize and pipeline) from dataflow, and encapsulated them as a set of customization annotations and primitives.<n>We conduct comprehensive experiments on commonly-used devices, across numerous experiments, our evaluation shows that TileLang can achieve state-of-the-art performance in key kernels.
  arXiv  Detail & Related papers  (2025-04-24T14:08:49Z)
• ML-Triton, A Multi-Level Compilation and Language Extension to Triton GPU Programming [2.4665562732779773]
  Triton is a DSL that offers a more user-friendly and portable alternative by programming at a higher level.<n>We propose ML-Triton which features multi-level compilation flow and programming interface.<n>Our approach achieves performance above 95% of expert-written kernels on Intel GPU.
  arXiv  Detail & Related papers  (2025-03-19T08:31:39Z)
• FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
  Decentralized training faces significant challenges regarding system design and efficiency. We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs) We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
  arXiv  Detail & Related papers  (2024-10-16T16:13:19Z)
• FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs [57.12856172329322]
  We envision a decentralized system unlocking the potential vast untapped consumer-level GPU. This system faces critical challenges, including limited CPU and GPU memory, low network bandwidth, the variability of peer and device heterogeneity.
  arXiv  Detail & Related papers  (2023-09-03T13:27:56Z)
• Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures [67.47328776279204]
  This work introduces a framework to develop efficient, portable Deep Learning and High Performance Computing kernels. We decompose the kernel development in two steps: 1) Expressing the computational core using Processing Primitives (TPPs) and 2) Expressing the logical loops around TPPs in a high-level, declarative fashion. We demonstrate the efficacy of our approach using standalone kernels and end-to-end workloads that outperform state-of-the-art implementations on diverse CPU platforms.
  arXiv  Detail & Related papers  (2023-04-25T05:04:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.