Nowadays, GPUs have achieved high throughput computing by running plenty of threads. However, owing to disjoint memory spaces of discrete CPU-GPU systems, exploiting CPU and GPU within a data processing pipeline is a non-trivial issue, which can only be resolved by the coarse-grained workflow of “copy-kernel-copy” or its variants in essence. There is an underlying bottleneck caused by frequent inter-processor invocations for fine-grained batch sizes. This article presents XeFlow that enables streamlined execution by leveraging hardware mechanisms inside new generation GPUs. XeFlow significantly reduces costly explicit copy and kernel launching within existing fashions. As an alternative, XeFlow introduces persistent operators that continuously process data through shared topics, which establish efficient inter-processor data channels via hardware page faults. Compared with the default “copy-kernel-copy” method, XeFlow shows up to $2.4\times \!\sim \!3.1\times$2.4×∼3.1× performance advantages in both coarse-grained and fine-grained pipeline execution. To demonstrate its potentials, this article also evaluates two GPU-accelerated applications, including data encoding and OLAP query.

中文翻译：

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XeFlow：为离散 CPU-GPU 平台简化处理器间管道执行

如今，GPU 通过运行大量线程实现了高吞吐量计算。然而，由于离散 CPU-GPU 系统的内存空间不相交，在数据处理管道中利用 CPU 和 GPU 是一个非同寻常的问题，只能通过“复制-内核-复制”或“复制-内核-复制”的粗粒度工作流来解决。本质上是它的变种。存在由处理器间频繁调用细粒度批次大小导致的潜在瓶颈。本文介绍XeFlow通过利用新一代 GPU 内的硬件机制来简化执行。XeFlow 显着减少了现有方式中成本高昂的显式复制和内核启动。作为替代方案，XeFlow 引入了持久操作符 不断地处理数据 共享主题，通过硬件页面错误建立高效的处理器间数据通道。与默认的“copy-kernel-copy”方式相比，XeFlow 最多显示$2.4\times \!\sim \!3.1\times$2.4×～3.1×粗粒度和细粒度管道执行的性能优势。为了展示其潜力，本文还评估了两个 GPU 加速应用程序，包括数据编码和 OLAP 查询。