{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-07-28T06:20:51.496Z","headline":"TileLang：用高级Python DSL通过TVM设计高性能GPU内核--从Tensor-Core GEMM到FlashAttention","description":"TileLang作为基于TVM的Python领域特定语言，可设计并编译面向性能的GPU内核。教程逐步实现向量加法、分块Tensor-Core矩阵乘法、融合GEMM后处理、行级Softmax及FlashAttention，并对比PyTorch和cuBLAS基线。通过自动调优识别架构相关内核配置，TileLang管理线程映射、内存布局、同步及底层CUDA指令生成。","url":"https://www.aioga.com/news/cms0pb4r10080rohp0yh1v4uf/","mainEntityOfPage":"https://www.aioga.com/news/cms0pb4r10080rohp0yh1v4uf/","datePublished":"2026-07-25T18:08:12.000Z","dateModified":"2026-07-25T18:08:12.000Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://www.marktechpost.com/2026/07/25/designing-high-performance-gpu-kernels-with-tilelang-tensor-core-gemm-fused-softmax-flashattention-and-autotuning","https://aihot.virxact.com/items/cms0pb4r10080rohp0yh1v4uf"],"canonicalUrl":"https://www.aioga.com/news/cms0pb4r10080rohp0yh1v4uf/","directAnswer":{"@type":"Answer","text":"Aioga 编辑摘要：TileLang作为基于TVM的Python领域特定语言，可设计并编译面向性能的GPU内核。 Aioga 将其归入「技巧观点」方向，重点关注它对真实使用和行业竞争的影响。","url":"https://www.aioga.com/news/cms0pb4r10080rohp0yh1v4uf/","dateCreated":"2026-07-25T18:08:12.000Z","author":{"@type":"Organization","@id":"https://www.aioga.com/authors/aioga-editorial/#editorial-team","name":"Aioga Editorial Team","url":"https://www.aioga.com/authors/aioga-editorial/"}},"evidence":[{"@type":"CreativeWork","name":"marktechpost.com source article","url":"https://www.marktechpost.com/2026/07/25/designing-high-performance-gpu-kernels-with-tilelang-tensor-core-gemm-fused-softmax-flashattention-and-autotuning","datePublished":"2026-07-25T18:08:12.000Z","provider":{"@type":"Organization","name":"marktechpost.com","url":"https://www.marktechpost.com/2026/07/25/designing-high-performance-gpu-kernels-with-tilelang-tensor-core-gemm-fused-softmax-flashattention-and-autotuning"}},{"@type":"CreativeWork","name":"AIHot archive record","url":"https://aihot.virxact.com/items/cms0pb4r10080rohp0yh1v4uf","datePublished":"2026-07-25T18:08:12.000Z","provider":{"@type":"Organization","name":"AIHot","url":"https://aihot.virxact.com/items/cms0pb4r10080rohp0yh1v4uf"}}],"aggregationSource":"MarkTechPost（RSS）","originalPublisher":{"name":"marktechpost.com","url":"https://www.marktechpost.com/2026/07/25/designing-high-performance-gpu-kernels-with-tilelang-tensor-core-gemm-fused-softmax-flashattention-and-autotuning"},"article":{"id":"cms0pb4r10080rohp0yh1v4uf","slug":"cms0pb4r10080rohp0yh1v4uf","url":"https://www.aioga.com/news/cms0pb4r10080rohp0yh1v4uf/","title":"TileLang：用高级Python DSL通过TVM设计高性能GPU内核--从Tensor-Core GEMM到FlashAttention","title_en":"Designing High-Performance GPU Kernels with TileLang： Tensor-Core GEMM， Fused Softmax， FlashAttention， and Autotuning","summary":"TileLang作为基于TVM的Python领域特定语言，可设计并编译面向性能的GPU内核。教程逐步实现向量加法、分块Tensor-Core矩阵乘法、融合GEMM后处理、行级Softmax及FlashAttention，并对比PyTorch和cuBLAS基线。通过自动调优识别架构相关内核配置，TileLang管理线程映射、内存布局、同步及底层CUDA指令生成。","source":"MarkTechPost（RSS）","sourceUrl":"https://www.marktechpost.com/2026/07/25/designing-high-performance-gpu-kernels-with-tilelang-tensor-core-gemm-fused-softmax-flashattention-and-autotuning","aiHotUrl":"https://aihot.virxact.com/items/cms0pb4r10080rohp0yh1v4uf","publishedAt":"2026-07-25T18:08:12.000Z","category":"技巧观点","score":61,"selected":false,"articleBody":["In this tutorial, we explore TileLang ：https://github.com/tile-ai/tilelang as a high-level Python domain-specific language for designing and compiling performance-oriented GPU kernels through TVM. We begin by validating the CUDA environment and establishing reusable benchmarking and numerical-verification utilities, then progressively implement vector addition, tiled tensor-core matrix multiplication, schedule exploration, fused GEMM epilogues, row-wise softmax, and FlashAttention. Throughout the tutorial, we work directly with TileLang’s shared-memory tiles, register fragments, pipelined loops, parallel iteration primitives, reductions, and tensor-core GEMM operators while allowing the compiler to manage thread mapping, memory layouts, synchronization, vectorization, and low-level CUDA instruction generation. We also compare our kernels against PyTorch and cuBLAS baselines, inspect generated CUDA source, evaluate memory and compute throughput, and use autotuning to identify architecture-dependent kernel configurations.","We configure the Google Colab CUDA environment, install TileLang with a nightly fallback, and import the required PyTorch and TileLang modules. We define reusable benchmarking, validation, and reporting utilities that measure kernel latency and compare numerical outputs using relative error. We then implement a TileLang vector-add kernel, execute it on the GPU, compare its bandwidth with PyTorch, and inspect the CUDA source generated by the compiler.","We implement a tiled tensor-core matrix multiplication kernel that moves input tiles through global memory, shared memory, and register fragments. We control tile dimensions, pipeline stages, thread counts, and L2 swizzling while allowing TileLang to generate tensor-core instructions, synchronization, and memory-transfer logic. We then benchmark several schedule configurations, verify their numerical accuracy, and identify the highest-performing architecture-dependent kernel configuration.","We extend the matrix multiplication kernel by fusing bias addition and the GELU activation directly into the register-resident accumulator. We reduce intermediate global-memory traffic by completing the epilogue before writing the final output tensor and compare the fused implementation with eager PyTorch execution. We also implement a row-wise softmax kernel using fragment-level maximum and sum reductions while keeping the normalization process largely within registers.","We implement a fused FlashAttention forward kernel that processes query, key, and value tiles without materializing the full attention-score matrix in global memory. We apply online softmax updates using running maxima, normalization sums, rescaling factors, and tiled tensor-core matrix multiplications. We validate both causal and non-causal attention against PyTorch scaled dot-product attention and compare their latency and computational throughput.","We define an autotuning search space across matrix tile sizes, K-block dimensions, pipeline depths, and thread counts while filtering configurations that exceed the shared-memory budget. We use TileLang’s autotuning decorator to compile, benchmark, validate, and cache multiple kernel schedules for the same matrix-multiplication workload. We then execute the selected kernel, verify its output against PyTorch, and report the achieved latency and tensor-core throughput.","We introduce TileLang’s debugging and introspection workflow through device-side printing, generated CUDA inspection, and the built-in kernel profiler. We examine compiler-emitted landmarks such as tensor-core operations, asynchronous copies, synchronization barriers, and matrix-load instructions. We finally organize all tutorial sections into a fault-tolerant runner that records execution status, reports timing information, and prints a compact TileLang programming reference.","In conclusion, we built a practical understanding of how TileLang translates tile-level Python programs into optimized GPU kernels without requiring us to manually manage thread indices, warp-level data layouts, tensor-core instructions, or asynchronous memory barriers. We implemented and validated kernels that cover bandwidth-bound elementwise operations, compute-intensive GEMM workloads, fused neural-network epilogues, register-resident reductions, and online-softmax attention. We also examined how block dimensions, shared-memory consumption, pipeline depth, thread count, tile shape, and L2 swizzling influence performance across different GPU architectures. Finally, we used generated-source inspection, device-side debugging, profiling, and automated schedule search to establish a complete workflow for developing, verifying, benchmarking, and refining custom TileLang kernels.","Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us ：https://forms.gle/wbash1wF6efRj8G58","Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions."],"articleImages":[],"mediaStatus":"none","articleBodyZh":["在本教程中，我们将探索 TileLang：https://github.com/tile-ai/tilelang，它是一种高层次的 Python 领域专用语言，用于通过 TVM 设计和编译面向性能的 GPU 内核。我们首先验证 CUDA 环境，并建立可重复使用的基准测试和数值验证工具，然后逐步实现向量加法、分块张量核心矩阵乘法、调度探索、融合 GEMM 尾处理、行级 softmax 以及 FlashAttention。在整个教程中，我们直接使用 TileLang 的共享内存块、寄存器片段、流水线循环、并行迭代原语、归约操作和张量核心 GEMM 运算符，同时让编译器管理线程映射、内存布局、同步、向量化以及低级 CUDA 指令生成。我们还将我们的内核与 PyTorch 和 cuBLAS 基线进行比较，检查生成的 CUDA 源码，评估内存和计算吞吐量，并使用自动调优来确定与架构相关的内核配置。","我们配置 Google Colab CUDA 环境，安装具有 nightly 回退的 TileLang，并导入所需的 PyTorch 和 TileLang 模块。我们定义了可重用的基准测试、验证和报告工具，用于测量内核延迟并使用相对误差比较数值输出。然后，我们实现了一个 TileLang 向量加法内核，在 GPU 上执行它，将其带宽与 PyTorch 进行比较，并检查编译器生成的 CUDA 源码。","我们实现了一个分块张量核心矩阵乘法内核，该内核通过全局内存、共享内存和寄存器片段移动输入块。我们控制块尺寸、流水线阶段、线程数量和 L2 打乱，同时允许 TileLang 生成张量核心指令、同步及内存传输逻辑。然后我们对多种调度配置进行了基准测试，验证了它们的数值精度，并确定了性能最高的与架构相关的内核配置。","我们通过将偏置加法和GELU激活直接融合到寄存器驻留的累加器中来扩展矩阵乘法内核。通过在写入最终输出张量之前完成尾部处理，我们减少了中间全局内存流量，并将融合实现与即时PyTorch执行进行比较。我们还实现了一个行级softmax内核，使用片段级最大值和求和归约，同时保持归一化过程主要在寄存器中进行。","我们实现了一个融合的FlashAttention前向内核，该内核处理查询、键和值的切片，而无需在全局内存中生成完整的注意力分数矩阵。我们使用运行最大值、归一化和求和、重缩放因子以及分块张量核心矩阵乘法来应用在线softmax更新。我们对因果和非因果注意力进行验证，与PyTorch的缩放点积注意力进行比较，并对它们的延迟和计算吞吐量进行分析。","我们定义了一个自调优搜索空间，涵盖矩阵切片大小、K块维度、流水线深度和线程数，同时过滤掉超过共享内存预算的配置。我们使用TileLang的自调优装饰器来编译、基准测试、验证和缓存相同矩阵乘法工作负载的多个内核调度。随后我们执行所选内核，将其输出与PyTorch进行验证，并报告实现的延迟和张量核心吞吐量。","我们介绍了TileLang的调试和自检工作流程，包括设备端打印、生成的CUDA检查以及内置的内核分析器。我们检查编译器生成的标志，如张量核心操作、异步拷贝、同步栅栏和矩阵加载指令。最后，我们将所有教程部分组织到一个容错执行器中，该执行器记录执行状态、报告计时信息，并打印紧凑的TileLang编程参考。","总之，我们建立了对 TileLang 的实际理解，它能够将基于 tile 的 Python 程序转换为优化的 GPU 内核，而无需我们手动管理线程索引、warp 级数据布局、张量核指令或异步内存屏障。我们实现并验证了涵盖带宽受限的逐元素操作、计算密集型 GEMM 工作量、融合神经网络尾处理、寄存器驻留的归约以及在线 softmax 注意力的内核。我们还研究了块维度、共享内存消耗、流水线深度、线程数量、tile 形状以及 L2 swizzling 如何影响不同 GPU 架构上的性能。最后，我们使用生成的源代码检查、设备端调试、性能分析和自动调度搜索，建立了开发、验证、基准测试和改进自定义 TileLang 内核的完整工作流程。","需要与我们合作来推广您的 GitHub 仓库或 Hugging Face 页面或产品发布或网络研讨会等吗？请与我们联系：https://forms.gle/wbash1wF6efRj8G58","Sana Hassan，是 Marktechpost 的咨询实习生，也是 IIT Madras 的双学位学生，他热衷于将技术和人工智能应用于解决现实世界的挑战。凭借对解决实际问题的浓厚兴趣，他为 AI 与现实生活解决方案的交汇带来了新的视角。"],"translationStatus":"translated","bodyOrigin":"source-page","editorial":{"summary":"Aioga 编辑摘要：TileLang作为基于TVM的Python领域特定语言，可设计并编译面向性能的GPU内核。 Aioga 将其归入「技巧观点」方向，重点关注它对真实使用和行业竞争的影响。","background":"背景分析：实践类内容的价值在于是否能被复现、是否有明确边界，以及它能否转化为稳定的开发或工作流方法。","viewpoint":"Aioga 判断：这条动态更适合作为行业观察信号，当前信息足以建立线索，但不足以推导长期结论。","implications":"影响分析：对相关团队而言，短期应先核对来源、可用范围和实际成本，再判断是否值得接入或跟进。","nextStep":"后续观察：继续观察示例是否可复现、工具版本变化、社区反馈和实际成本。","evidenceRefs":["title","summary","articleBody"],"confidence":"medium","status":"published","aiGenerated":false,"autoApproved":true,"generatedBy":"rule-safe-fallback","generatedAt":"2026-07-28T06:29:10.505Z","sourceHash":"348342fab2390e46","validation":{"passed":true,"mode":"rule-safe-fallback","checks":["schema","length","source-attribution","no-html"]}},"tags":["技巧观点","MarkTechPost（RSS）"],"translations":{"zh-CN":{"title":"TileLang：用高级Python DSL通过TVM设计高性能GPU内核--从Tensor-Core GEMM到FlashAttention","summary":"TileLang作为基于TVM的Python领域特定语言，可设计并编译面向性能的GPU内核。教程逐步实现向量加法、分块Tensor-Core矩阵乘法、融合GEMM后处理、行级Softmax及FlashAttention，并对比PyTorch和cuBLAS基线。通过自动调优识别架构相关内核配置，TileLang管理线程映射、内存布局、同步及底层CUDA指令生成。","category":"技巧观点","source":"marktechpost.com","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang：用高级Python DSL通过TVM设计高性能GPU内核--从Tensor-Core GEMM到FlashAttention - Aioga AI资讯","description":"TileLang作为基于TVM的Python领域特定语言，可设计并编译面向性能的GPU内核。教程逐步实现向量加法、分块Tensor-Core矩阵乘法、融合GEMM后处理、行级Softmax及FlashAttention，并对比PyTorch和cuBLAS基线。通过自动调优识别架构相关内核配置，TileLang管理线程映射、内存布局、同步及底层CUDA指令生成...","url":"https://www.aioga.com/news/cms0pb4r10080rohp0yh1v4uf/"},"en":{"title":"TileLang: Designing High-Performance GPU Kernels with an Advanced Python DSL via TVM -- From Tensor-Core GEMM to FlashAttention","summary":"TileLang, as a TVM-based Python domain-specific language, can design and compile performance-oriented GPU kernels. The tutorial incrementally implements vector addition, blocked Tensor-Core matrix multiplication, fused GEMM post-processing, row-wise Softmax, and FlashAttention, while comparing with PyTorch and cuBLAS baselines. Through automatic tuning, architecture-specific kernel configurations are identified, and TileLang manages thread mapping, memory layout, synchronization, and low-level CUDA instruction generation.","category":"Insights","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: Designing High-Performance GPU Kernels with an Advanced Python DSL via TVM -- From Tensor-Core GEMM to FlashAttention - Aioga AI News","description":"TileLang, as a TVM-based Python domain-specific language, can design and compile performance-oriented GPU kernels. The tutorial incrementally implements vector addition, blocked Te...","url":"https://www.aioga.com/en/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:42:01.912Z"},"ja":{"title":"TileLang：高級Python DSLを使用してTVMで高性能GPUカーネルを設計する--Tensor-Core GEMMからFlashAttentionまで","summary":"TileLangは、TVMに基づくPythonのドメイン固有言語として、性能指向のGPUカーネルを設計・コンパイルすることができます。チュートリアルでは、ベクトル加算、ブロック分割Tensor-Core行列乗算、GEMM後処理の融合、行単位Softmax、およびFlashAttentionを順を追って実装し、PyTorchとcuBLASのベースラインと比較します。自動チューニングによりアーキテクチャ関連のカーネル構成を特定し、TileLangはスレッドマッピング、メモリレイアウト、同期、および低レベルのCUDA命令生成を管理します。","category":"ヒントと視点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang：高級Python DSLを使用してTVMで高性能GPUカーネルを設計する--Tensor-Core GEMMからFlashAttentionまで - Aioga AIニュース","description":"TileLangは、TVMに基づくPythonのドメイン固有言語として、性能指向のGPUカーネルを設計・コンパイルすることができます。チュートリアルでは、ベクトル加算、ブロック分割Tensor-Core行列乗算、GEMM後処理の融合、行単位Softmax、およびFlashAttentionを順を追って実装し、PyTorchとcuBLASのベースラインと比較し...","url":"https://www.aioga.com/ja/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:42:08.915Z"},"ko":{"title":"TileLang: 고급 Python DSL을 사용하여 TVM으로 고성능 GPU 커널 설계하기--Tensor-Core GEMM에서 FlashAttention까지","summary":"TileLang은 TVM 기반의 Python 도메인 특화 언어로서, 성능 중심의 GPU 커널을 설계하고 컴파일할 수 있습니다. 이 튜토리얼에서는 벡터 더하기, 블록화된 Tensor-Core 행렬 곱셈, GEMM 후처리 융합, 행 단위 Softmax, FlashAttention을 단계별로 구현하고, PyTorch와 cuBLAS 기준과 비교합니다. 자동 튜닝을 통해 아키텍처 관련 커널 구성을 식별하며, TileLang은 스레드 매핑, 메모리 배치, 동기화 및 하위 CUDA 명령어 생성을 관리합니다.","category":"인사이트","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: 고급 Python DSL을 사용하여 TVM으로 고성능 GPU 커널 설계하기--Tensor-Core GEMM에서 FlashAttention까지 - Aioga AI 뉴스","description":"TileLang은 TVM 기반의 Python 도메인 특화 언어로서, 성능 중심의 GPU 커널을 설계하고 컴파일할 수 있습니다. 이 튜토리얼에서는 벡터 더하기, 블록화된 Tensor-Core 행렬 곱셈, GEMM 후처리 융합, 행 단위 Softmax, FlashAttention을 단계별로 구현하고, PyTorch와 cuBL...","url":"https://www.aioga.com/ko/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:42:53.927Z"},"es":{"title":"TileLang: Diseñando núcleos GPU de alto rendimiento con un DSL avanzado de Python a través de TVM: desde Tensor-Core GEMM hasta FlashAttention","summary":"TileLang, como un lenguaje específico de dominio en Python basado en TVM, puede diseñar y compilar núcleos de GPU orientados al rendimiento. El tutorial implementa paso a paso la suma de vectores, la multiplicación de matrices en bloques con Tensor-Core, la fusión del postprocesamiento de GEMM, Softmax a nivel de fila y FlashAttention, y compara con las referencias de PyTorch y cuBLAS. Mediante la autooptimización se identifican configuraciones de núcleos relacionadas con la arquitectura, y TileLang gestiona el mapeo de hilos, la disposición de la memoria, la sincronización y la generación de instrucciones CUDA subyacentes.","category":"Ideas","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: Diseñando núcleos GPU de alto rendimiento con un DSL avanzado de Python a través de TVM: desde Tensor-Core GEMM hasta FlashAttention - Aioga Noticias de IA","description":"TileLang, como un lenguaje específico de dominio en Python basado en TVM, puede diseñar y compilar núcleos de GPU orientados al rendimiento. El tutorial implementa paso a paso la s...","url":"https://www.aioga.com/es/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:42:47.966Z"},"fr":{"title":"TileLang : Concevoir des noyaux GPU haute performance via TVM avec un DSL Python avancé -- du GEMM Tensor-Core à FlashAttention","summary":"TileLang, en tant que langage spécifique au domaine Python basé sur TVM, permet de concevoir et de compiler des noyaux GPU orientés performance. Le tutoriel réalise étape par étape l'addition de vecteurs, la multiplication de matrices Tensor-Core par blocs, la fusion du post-traitement GEMM, le Softmax au niveau des lignes et FlashAttention, et compare avec les références PyTorch et cuBLAS. Grâce à l'auto-ajustement, il identifie les configurations de noyaux liées à l'architecture, et TileLang gère la cartographie des threads, la disposition de la mémoire, la synchronisation et la génération des instructions CUDA de bas niveau.","category":"Analyses","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang : Concevoir des noyaux GPU haute performance via TVM avec un DSL Python avancé -- du GEMM Tensor-Core à FlashAttention - Aioga Actualités IA","description":"TileLang, en tant que langage spécifique au domaine Python basé sur TVM, permet de concevoir et de compiler des noyaux GPU orientés performance. Le tutoriel réalise étape par étape...","url":"https://www.aioga.com/fr/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:43:36.406Z"},"de":{"title":"TileLang: Hochleistungs-GPU-Kerne mit einer fortgeschrittenen Python-DSL über TVM entwerfen – von Tensor-Core GEMM bis FlashAttention","summary":"TileLang ist als auf TVM basierende Python-Domänenspezifische Sprache in der Lage, leistungsorientierte GPU-Kerne zu entwerfen und zu kompilieren. Das Tutorial implementiert schrittweise Vektoraddition, blockweise Tensor-Core-Matrixmultiplikation, fusionierte GEMM-Nachverarbeitung, zeilenweises Softmax und FlashAttention und vergleicht diese mit den PyTorch- und cuBLAS-Baselines. Durch automatisches Tuning werden architekturbezogene Kernel-Konfigurationen erkannt, wobei TileLang Thread-Mapping, Speicherlayout, Synchronisation und die Generierung von untergeordneten CUDA-Befehlen verwaltet.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: Hochleistungs-GPU-Kerne mit einer fortgeschrittenen Python-DSL über TVM entwerfen – von Tensor-Core GEMM bis FlashAttention - Aioga KI-News","description":"TileLang ist als auf TVM basierende Python-Domänenspezifische Sprache in der Lage, leistungsorientierte GPU-Kerne zu entwerfen und zu kompilieren. Das Tutorial implementiert schrit...","url":"https://www.aioga.com/de/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:43:32.292Z"},"pt-BR":{"title":"TileLang: Usando DSL Python avançado para projetar núcleos GPU de alto desempenho com TVM - de Tensor-Core GEMM a FlashAttention","summary":"TileLang, como uma linguagem específica de domínio Python baseada em TVM, pode projetar e compilar núcleos de GPU voltados para desempenho. O tutorial implementa passo a passo adição de vetores, multiplicação de matrizes com Tensor-Core em blocos, fusão de pós-processamento GEMM, Softmax em nível de linha e FlashAttention, e compara com as bases do PyTorch e cuBLAS. Através de ajuste automático, identifica a configuração de núcleos relacionada à arquitetura, e TileLang gerencia mapeamento de threads, layout de memória, sincronização e geração de instruções CUDA de baixo nível.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: Usando DSL Python avançado para projetar núcleos GPU de alto desempenho com TVM - de Tensor-Core GEMM a FlashAttention - Aioga Notícias de IA","description":"TileLang, como uma linguagem específica de domínio Python baseada em TVM, pode projetar e compilar núcleos de GPU voltados para desempenho. O tutorial implementa passo a passo adiç...","url":"https://www.aioga.com/pt-BR/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:44:15.731Z"},"ru":{"title":"TileLang: проектирование высокопроизводительных GPU-ядр с помощью продвинутого Python DSL через TVM — от Tensor-Core GEMM до FlashAttention","summary":"TileLang, будучи специализированным языком Python на базе TVM, позволяет проектировать и компилировать ориентированные на производительность GPU-ядра. В учебнике пошагово реализуются сложение векторов, блочное умножение матриц с использованием Tensor-Core, объединённая постобработка GEMM, поэлементный Softmax и FlashAttention, а также проводится сравнение с базовыми реализациями PyTorch и cuBLAS. С помощью автоматической настройки выявляются конфигурации ядер, связанные с архитектурой, TileLang управляет отображением потоков, расположением в памяти, синхронизацией и генерацией низкоуровневых инструкций CUDA.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: проектирование высокопроизводительных GPU-ядр с помощью продвинутого Python DSL через TVM — от Tensor-Core GEMM до FlashAttention - Aioga Новости ИИ","description":"TileLang, будучи специализированным языком Python на базе TVM, позволяет проектировать и компилировать ориентированные на производительность GPU-ядра. В учебнике пошагово реализуют...","url":"https://www.aioga.com/ru/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:44:14.621Z"},"ar":{"title":"TileLang: استخدام DSL بايثون متقدم لتصميم نوى GPU عالية الأداء عبر TVM -- من Tensor-Core GEMM إلى FlashAttention","summary":"TileLang، باعتباره لغة مخصصة لمجال بايثون تعتمد على TVM، يمكنه تصميم وتجميع نوى GPU موجهة للأداء. يقدم الدليل التعليمي تنفيذ خطوة بخطوة لجمع المتجهات، وضرب مصفوفات Tensor-Core على شكل كتل، ودمج معالجات GEMM اللاحقة، وSoftmax على مستوى الصف وFlashAttention، كما يقارن مع الأساسيات في PyTorch وcuBLAS. من خلال الضبط التلقائي، يتم التعرف على تكوين النوى المتعلقة بالمعمارية، ويدير TileLang تعيين الخيوط، وتخطيط الذاكرة، والمزامنة، وإنشاء تعليمات CUDA الأساسية.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: استخدام DSL بايثون متقدم لتصميم نوى GPU عالية الأداء عبر TVM -- من Tensor-Core GEMM إلى FlashAttention - Aioga أخبار الذكاء الاصطناعي","description":"TileLang، باعتباره لغة مخصصة لمجال بايثون تعتمد على TVM، يمكنه تصميم وتجميع نوى GPU موجهة للأداء. يقدم الدليل التعليمي تنفيذ خطوة بخطوة لجمع المتجهات، وضرب مصفوفات Tensor-Core على...","url":"https://www.aioga.com/ar/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:44:56.720Z"},"hi":{"title":"TileLang: उच्च-स्तरीय Python DSL का उपयोग करके TVM के माध्यम से उच्च-प्रदर्शन GPU कर्नल डिज़ाइन करना -- Tensor-Core GEMM से लेकर FlashAttention तक","summary":"TileLang, TVM पर आधारित Python डोमेन-विशिष्ट भाषा के रूप में, प्रदर्शन-उन्मुख GPU कर्नेल को डिज़ाइन और संकलित कर सकता है। ट्यूटोरियल चरण दर चरण वेक्टर जोड़, ब्लॉक-आधारित टेन्सर-कोर मैट्रिक्स गुणा, GEMM पोस्ट-प्रोसेसिंग का फ्यूज़, रो-स्तरीय Softmax और FlashAttention को लागू करता है, और PyTorch और cuBLAS बेसलाइन की तुलना करता है। स्वचालित ट्यूनिंग के माध्यम से आर्किटेक्चर-संबंधित कर्नेल कॉन्फ़िगरेशन की पहचान करने पर, TileLang थ्रेड मैपिंग, मेमोरी लेआउट, सिंक और निचले स्तर के CUDA इंस्ट्रक्शन्स उत्पन्न करता है।","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: उच्च-स्तरीय Python DSL का उपयोग करके TVM के माध्यम से उच्च-प्रदर्शन GPU कर्नल डिज़ाइन करना -- Tensor-Core GEMM से लेकर FlashAttention तक - Aioga AI समाचार","description":"TileLang, TVM पर आधारित Python डोमेन-विशिष्ट भाषा के रूप में, प्रदर्शन-उन्मुख GPU कर्नेल को डिज़ाइन और संकलित कर सकता है। ट्यूटोरियल चरण दर चरण वेक्टर जोड़, ब्लॉक-आधारित टेन्सर-कोर...","url":"https://www.aioga.com/hi/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:45:00.626Z"},"it":{"title":"TileLang: progettare kernel GPU ad alte prestazioni tramite TVM usando un DSL Python avanzato -- da Tensor-Core GEMM a FlashAttention","summary":"TileLang, come linguaggio specifico per dominio basato su TVM in Python, può progettare e compilare kernel GPU orientati alle prestazioni. Il tutorial realizza passo passo l'addizione di vettori, la moltiplicazione di matrici Tensor-Core a blocchi, la fusione del post-processing GEMM, il Softmax a livello di riga e FlashAttention, confrontandoli con i baseline di PyTorch e cuBLAS. Attraverso l'auto-tuning si identificano le configurazioni dei kernel correlate all'architettura; TileLang gestisce la mappatura dei thread, la disposizione della memoria, la sincronizzazione e la generazione delle istruzioni CUDA a basso livello.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: progettare kernel GPU ad alte prestazioni tramite TVM usando un DSL Python avanzato -- da Tensor-Core GEMM a FlashAttention - Aioga Notizie IA","description":"TileLang, come linguaggio specifico per dominio basato su TVM in Python, può progettare e compilare kernel GPU orientati alle prestazioni. Il tutorial realizza passo passo l'addizi...","url":"https://www.aioga.com/it/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:45:44.521Z"},"nl":{"title":"TileLang: Gebruik geavanceerde Python DSL om hoge-prestatie GPU-kernen te ontwerpen met TVM -- van Tensor-Core GEMM tot FlashAttention","summary":"TileLang, als een op TVM gebaseerde Python-domeinspecifieke taal, kan prestatiegerichte GPU-kernels ontwerpen en compileren. De handleiding implementeert stapsgewijs vectoroptelling, geblokkeerde Tensor-Core matrixvermenigvuldiging, gefuseerde GEMM-nabewerking, rij-niveau Softmax en FlashAttention, en vergelijkt deze met PyTorch- en cuBLAS-baselines. Door automatische afstemming worden architectuurgerelateerde kernelconfiguraties geïdentificeerd, en TileLang beheert threadmapping, geheugenindeling, synchronisatie en de generatie van onderliggende CUDA-instructies.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: Gebruik geavanceerde Python DSL om hoge-prestatie GPU-kernen te ontwerpen met TVM -- van Tensor-Core GEMM tot FlashAttention - Aioga AI-nieuws","description":"TileLang, als een op TVM gebaseerde Python-domeinspecifieke taal, kan prestatiegerichte GPU-kernels ontwerpen en compileren. De handleiding implementeert stapsgewijs vectoroptellin...","url":"https://www.aioga.com/nl/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:45:39.442Z"},"tr":{"title":"TileLang: Yüksek Performanslı GPU Çekirdeğini TVM ile Gelişmiş Python DSL Kullanarak Tasarlamak -- Tensor-Core GEMM'den FlashAttention'a","summary":"TileLang, TVM tabanlı bir Python alanına özgü dil olarak, performans odaklı GPU çekirdeklerini tasarlayıp derleyebilir. Eğitim, adım adım vektör toplamanın, bloklu Tensor-Core matris çarpımının, GEMM sonrası birleşiminin, satır seviyesinde Softmax ve FlashAttention'un uygulanmasını gösterir ve PyTorch ile cuBLAS temelleriyle karşılaştırır. Otomatik ayarlama yoluyla mimariye bağlı çekirdek yapılandırmalarını tanır; TileLang, iş parçacığı eşlemesini, bellek düzenini, senkronizasyonu ve alt düzey CUDA komutlarının üretimini yönetir.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: Yüksek Performanslı GPU Çekirdeğini TVM ile Gelişmiş Python DSL Kullanarak Tasarlamak -- Tensor-Core GEMM'den FlashAttention'a - Aioga AI Haberleri","description":"TileLang, TVM tabanlı bir Python alanına özgü dil olarak, performans odaklı GPU çekirdeklerini tasarlayıp derleyebilir. Eğitim, adım adım vektör toplamanın, bloklu Tensor-Core matr...","url":"https://www.aioga.com/tr/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:46:29.363Z"},"vi":{"title":"TileLang: Sử dụng DSL Python cao cấp để thiết kế nhân GPU hiệu suất cao qua TVM -- từ Tensor-Core GEMM đến FlashAttention","summary":"TileLang là ngôn ngữ đặc thù theo lĩnh vực (DSL) Python dựa trên TVM, có thể thiết kế và biên dịch các lõi GPU hướng tới hiệu năng. Hướng dẫn từng bước thực hiện cộng vector, nhân ma trận Tensor-Core theo khối, hợp nhất xử lý GEMM, Softmax theo hàng và FlashAttention, đồng thời so sánh với các mốc PyTorch và cuBLAS. Thông qua tự động tinh chỉnh, nhận diện cấu hình lõi liên quan kiến trúc, TileLang quản lý ánh xạ luồng, bố trí bộ nhớ, đồng bộ và sinh các lệnh CUDA cấp thấp.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: Sử dụng DSL Python cao cấp để thiết kế nhân GPU hiệu suất cao qua TVM -- từ Tensor-Core GEMM đến FlashAttention - Tin tức AI Aioga","description":"TileLang là ngôn ngữ đặc thù theo lĩnh vực (DSL) Python dựa trên TVM, có thể thiết kế và biên dịch các lõi GPU hướng tới hiệu năng. Hướng dẫn từng bước thực hiện cộng vector, nhân...","url":"https://www.aioga.com/vi/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:46:23.038Z"},"id":{"title":"TileLang: Mendesain Kernel GPU Berkinerja Tinggi dengan DSL Python Tingkat Lanjut melalui TVM -- dari Tensor-Core GEMM hingga FlashAttention","summary":"TileLang sebagai bahasa khusus domain Python berbasis TVM, dapat merancang dan mengompilasi kernel GPU yang berfokus pada performa. Tutorial secara bertahap mengimplementasikan penjumlahan vektor, perkalian matriks Tensor-Core berbasis blok, fusi pasca-pemrosesan GEMM, Softmax tingkat baris, dan FlashAttention, serta membandingkannya dengan baseline PyTorch dan cuBLAS. Melalui optimasi otomatis, mengidentifikasi konfigurasi kernel terkait arsitektur, TileLang mengelola pemetaan thread, tata letak memori, sinkronisasi, dan generasi instruksi CUDA tingkat rendah.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: Mendesain Kernel GPU Berkinerja Tinggi dengan DSL Python Tingkat Lanjut melalui TVM -- dari Tensor-Core GEMM hingga FlashAttention - Berita AI Aioga","description":"TileLang sebagai bahasa khusus domain Python berbasis TVM, dapat merancang dan mengompilasi kernel GPU yang berfokus pada performa. Tutorial secara bertahap mengimplementasikan pen...","url":"https://www.aioga.com/id/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:47:07.519Z"},"th":{"title":"TileLang: ใช้ DSL Python ขั้นสูงออกแบบเคอร์เนล GPU ประสิทธิภาพสูงผ่าน TVM--จาก Tensor-Core GEMM ถึง FlashAttention","summary":"TileLang ในฐานะภาษาด้านเฉพาะของ Python ที่สร้างบนพื้นฐาน TVM สามารถออกแบบและคอมไพล์เคอร์เนล GPU ที่เน้นประสิทธิภาพได้ คู่มือแนะนำขั้นตอนการทำเวกเตอร์บวก การคูณเมทริกซ์ Tensor-Core แบบแบ่งบล็อก การรวมการประมวลผลภายหลัง GEMM Softmax ระดับแถว และ FlashAttention พร้อมทั้งเปรียบเทียบกับ PyTorch และ cuBLAS ผ่านการปรับจูนอัตโนมัติเพื่อระบุการกำหนดค่าเคอร์เนลที่เกี่ยวข้องกับสถาปัตยกรรม TileLang จะจัดการการแมปเธรด การจัดวางหน่วยความจำ การซิงโครไนซ์ และการสร้างคำสั่ง CUDA ขั้นต่ำ","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: ใช้ DSL Python ขั้นสูงออกแบบเคอร์เนล GPU ประสิทธิภาพสูงผ่าน TVM--จาก Tensor-Core GEMM ถึง FlashAttention - ข่าว AI Aioga","description":"TileLang ในฐานะภาษาด้านเฉพาะของ Python ที่สร้างบนพื้นฐาน TVM สามารถออกแบบและคอมไพล์เคอร์เนล GPU ที่เน้นประสิทธิภาพได้ คู่มือแนะนำขั้นตอนการทำเวกเตอร์บวก การคูณเมทริกซ์ Tensor-Core...","url":"https://www.aioga.com/th/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:47:17.378Z"},"pl":{"title":"TileLang: Projektowanie wysokowydajnych rdzeni GPU przy użyciu zaawansowanego DSL w Pythonie poprzez TVM — od Tensor-Core GEMM do FlashAttention","summary":"TileLang, jako oparty na TVM specyficzny dla języka Python, pozwala projektować i kompilować wysokowydajne jądra GPU. Samouczek krok po kroku realizuje dodawanie wektorów, dzieloną macierzową mnożenie z użyciem Tensor-Core, zintegrowane przetwarzanie GEMM, softmax na poziomie wierszy oraz FlashAttention, i porównuje je z bazowymi rozwiązaniami PyTorch i cuBLAS. Poprzez automatyczną optymalizację identyfikowane są konfiguracje jąder zależne od architektury, a TileLang zarządza mapowaniem wątków, układem pamięci, synchronizacją oraz generowaniem niskopoziomowych instrukcji CUDA.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"TileLang: Projektowanie wysokowydajnych rdzeni GPU przy użyciu zaawansowanego DSL w Pythonie poprzez TVM — od Tensor-Core GEMM do FlashAttention - Aioga Wiadomości AI","description":"TileLang, jako oparty na TVM specyficzny dla języka Python, pozwala projektować i kompilować wysokowydajne jądra GPU. Samouczek krok po kroku realizuje dodawanie wektorów, dzieloną...","url":"https://www.aioga.com/pl/news/cms0pb4r10080rohp0yh1v4uf/","contentTranslated":true,"sourceHash":"d7b51158a89a5ee6","translatedAt":"2026-07-26T07:48:00.117Z"}}}}