GPU核子图匹配的相似性度量
A Similarity Measure for GPU Kernel Subgraph Matching
摘要：
加速器架构专门用于以锁步方式执行SIMD（单指令，多数据）。由于大多数CUDA应用程序都是并行循环，因此控制流信息可以提供内核的深入表征。CUDAflow是一个工具，它静态地将CUDA二进制文件分离为基本块区域，并动态地测量指令和基本块频率。CUDAflow在控制流图（CFG）中捕获此信息，并在各种内核的CFG之间执行子图匹配，以基于图的形状和遍历、执行的指令操作和分配的寄存器等信息了解应用程序的资源需求。CUDAflow的实用性通过各种GPU架构上的SHOC和Rodinia应用案例研究进行了演示，揭示了新颖的线程发散特性，这些特性有助于最终用户，自动调优器和编译器生成高性能代码。
英文摘要：
Accelerator architectures specialize in executing SIMD (single instruction, multiple data) in lockstep. Because the majority of CUDA applications are parallelized loops, control flow information can provide an in-depth characterization of a kernel. CUDAflow is a tool that statically separates CUDA binaries into basic block regions and dynamically measures instruction and basic block frequencies. CUDAflow captures this information in a control flow graph (CFG) and performs subgraph matching across various kernel's CFGs to gain insights to an application's resource requirements, based on the shape and traversal of the graph, instruction operations executed and registers allocated, among other information. The utility of CUDAflow is demonstrated with SHOC and Rodinia application case studies on a variety of GPU architectures, revealing novel thread divergence characteristics that facilitates end users, autotuners and compilers in generating high performing code.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
