XTC统一调度与性能评估，加速AI算子优化研究。
XTC, A Research Platform for Optimizing AI Workload Operators
- 提供通用API，解耦调度描述与代码生成
- 支持跨编译器的可复现性能测量
- 适合研究算子优化策略的开发者
AI算子高效运行需要对计算和数据移动进行精确控制。然而，现有调度语言被锁定在特定编译器生态中，阻碍了框架间的公平比较、复用与评估。目前尚无统一接口能将调度规范与代码生成及测量分离。我们提出XTC平台，实现跨编译器的调度与性能评估统一。通过通用API和可复现的测量框架，XTC支持可移植的实验，加速优化策略的研究。
原文摘要 · Abstract (English)
Achieving high efficiency on AI operators demands precise control over computation and data movement. However, existing scheduling languages are locked into specific compiler ecosystems, preventing fair comparison, reuse, and evaluation across frameworks. No unified interface currently decouples scheduling specification from code generation and measurement. We introduce XTC, a platform that unifies scheduling and performance evaluation across compilers. With its common API and reproducible measurement framework, XTC enables portable experimentation and accelerates research on optimization strategies.
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