Deep-learning throughput hinges on how effectively a compiler stack maps tensor programs to GPU execution: thread/block schedules, memory movement, and instruction selection (e.g., Tensor Core MMA pipelines). In this article we will focus on four dominant stacks—CUDA, ROCm, Triton, and TensorRT—from the compiler’s perspective and explains which optimizations move the needle in practice. What actually […]
The post Software Frameworks Optimized for GPUs in AI: CUDA, ROCm, Triton, TensorRT—Compiler Paths and Performance Implications appeared first on MarkTechPost.