一句话：提出 Baseliner——插件化、硬件无关的 GPU 基准测试框架，解耦工作负载定义与测量编排，并支持跨工具链复用同一 Workload。
GPU 基准测试是 HPC 性能论断的基础，但产生这些测量的软件基础设施很少被视为一等研究产物。现有工具碎片化：基准套件将测量逻辑与 kernel 耦合，厂商 profiler 面向诊断追踪而非统计式基准测试，生态专属库缺乏跨硬件可移植性。本文提出 Baseliner，一个硬件无关的 GPU 基准测试框架，其插件化架构将工作负载定义与测量编排解耦：形式化的 Workload Interface 与后端抽象层允许工作负载、计时后端、停止准则与统计引擎独立替换；适配器机制使同一 Workload 定义可由 Baseliner、NVBench 或 PrimBench 执行，消除工具链间的重复实现。作者以跨厂商 BLAS 套件 blas-baseliner（cuBLAS 与 rocBLAS）验证框架，在 NVIDIA V100 与 AMD MI210 上显示其达到 GPU event 计时的精度下限内与厂商库相当的测量结果，墙钟时间也具竞争力。
GPU benchmarking underpins performance claims across high-performance computing (HPC), yet the software infrastructure producing these measurements is rarely treated as a first-class research artifact. Existing tools fragment into benchmark suites where measurement logic is coupled to kernels, vendor profilers designed for diagnostic tracing rather than statistical benchmarking, and ecosystem-specific libraries that lack cross-hardware portability. We present Baseliner, a hardware-agnostic GPU benchmarking framework whose plugin-oriented architecture decouples workload definition from measurement orchestration. A formalized Workload Interface and a backend abstraction layer let workloads, timing backends, stopping criteria, and statistics engines be swapped independently of the core engine. An adapter mechanism further enables a single Workload definition to be executed by Baseliner, NVBench, or PrimBench, eliminating redundant implementations across toolchains. We validate the framework through blas-baseliner, a cross-vendor BLAS suite spanning cuBLAS and rocBLAS. Empirical evaluation on NVIDIA V100 and AMD MI210 hardware shows Baseliner reaches measurement parity with vendor libraries within the precision floor of GPU event timing while remaining competitive in wall-clock time. Video: https://youtu.be/LdKUEqewmQw