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This tutorial demonstrates how TileLang simplifies the development of high-performance GPU kernels for critical AI workloads like GEMM, FlashAttention, and fused softmax within the TVM ecosystem. By automating low-level CUDA generation while allowing explicit control over memory layouts and thread mapping, it bridges the gap between algorithmic design in Python and hardware-optimized execution on tensor cores. The inclusion of autotuning capabilities highlights a practical approach to optimizing compute throughput across different GPU architectures without manual instruction engineering.

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Designing High-Performance GPU Kernels with TileLang: Tensor-Core GEMM, Fused Softmax, FlashAttention, and Autotuning

MarkTechPost25 iulie 20264 min de cititArticolul și perspectiva sunt în engleză.

Explore TileLang, a high-level Python domain-specific language that simplifies the design of high-performance GPU kernels. This tutorial provides a step-by-step approach to implementing complex workloads—including tiled tensor-core GEMM, fused softmax, and FlashAttention—while letting the compiler handle intricate thread mapping, memory layouts, and low-level CUDA instruction generation. The post Designing High-Performance GPU Kernels with TileLang: Tensor-Core GEMM, Fused Softmax, FlashAttention, and Autotuning appeared first on MarkTechPost.

In this tutorial, we explore 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…

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