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首页> 外文会议>International Computer Conference, Computer Society of Iran >Machine Learning-based Interference Detection in GPGPU Concurrent Kernel Execution
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Machine Learning-based Interference Detection in GPGPU Concurrent Kernel Execution

机译：GPGPU并发内核执行中基于机器学习的干扰检测

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摘要

Recent advancements in GPU architectures have made it possible to run multiple kernels concurrently on a single GPU, to avoid under-utilization of its resources. Fine-grain sharing of streaming multiprocessors (SMs) allows thread blocks of multiple kernels to be assigned to GPU resources altogether. However, this may cause resource contention and performance degradation if both kernels try to access a shared resource at the same time. Detecting these interferences is essential especially in high-performance computing (HPC) systems, in which multiple applications may issue different kernels to available shared GPUs. This paper proposes a machine learning-based approach to characterize kernels and predict interference before their concurrent execution. Random forest classifier is used to classify interfering and noninterfering kernels. Experimental results show that the proposed method can detect interfering kernels with up to 91.7% accuracy.
机译：GPU架构中的最新进步使得可以同时在单个GPU上运行多个内核，以避免利用其资源。流媒体多处理器（SMS）的细粒度共享允许完全分配给GPU资源的多个内核的线程块。但是，如果两个内核尝试同时访问共享资源，则可能导致资源竞争和性能下降。检测这些干扰是必不可少的，特别是在高性能计算（HPC）系统中，其中多个应用程序可能会向可用的共享GPU发出不同的内核。本文提出了一种基于机器学习的方法来表征内核并在并发执行前预测干扰。随机森林分类器用于对干扰和非交叉内核进行分类。实验结果表明，该方法可以检测干扰核，精度高达91.7％。

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