Multitasking Real-time Embedded GPU Computing Tasks
Multitasking Real-time Embedded GPU Computing Tasks
复制标题
多任务实时嵌入式 GPU 计算任务
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
John Douglas Owens
中科院分区：
文献类型：
--
作者：
Pınar Muyan;John Douglas Owens
在这项研究中，我们考虑了涉及多个实时嵌入式GPU计算任务的工作负载的具体特征，并设计了几种使用替代方法的调度器。然后，我们比较调度器的性能，并确定对于给定的工作负载，哪种调度方法更有效以及为什么。本研究的主要结论包括：(A)同时运行小核对小核有利。(B)小内核、具有较长运行时间的高优先级内核和具有较短运行时间的低优先级内核的组合受益于动态改变费米体系结构上的内核顺序的CPU调度器。(C)由于现有GPU体系结构的限制，目前CPU调度器的性能优于GPU调度器。我们还强调了当前GPU架构在运行多个实时任务方面的不足，并推荐了可以改进调度的新功能，包括硬件优先级、抢占、可编程调度、通用时间概念和跨CPU和GPU的原子性。
In this study, we consider the specific characteristics of workloads that involve multiple real-time embedded GPU computing tasks and design several schedulers that use alternative approaches. Then, we compare the performance of schedulers and determine which scheduling approach is more effective for a given workload and why. The major conclusions of this study include: (a) Small kernels benefit from running kernels concurrently. (b) The combination of small kernels, high-priority kernels with longer runtimes, and lower-priority kernels with shorter runtimes benefits from a CPU scheduler that dynamically changes kernel order on the Fermi architecture. (c) Due to limitations of existing GPU architectures, currently CPU schedulers outperform their GPU counterparts. We also highlight the shortcomings of current GPU architectures with regard to running multiple real-time tasks, and recommend new features that would improve scheduling, including hardware priorities, preemption, programmable scheduling, and a common time concept and atomics across the CPU and GPU.