论文标题
可互操作的GPU内核作为MEC的延迟改良剂
Interoperable GPU Kernels as Latency Improver for MEC
论文作者
论文摘要
当5G成为主流时，混合现实（MR）应用将变得普遍。但是，由于基于视频的图形远程所需的资源，即解码视频编解码器所需的资源，延迟要求很具有挑战性。我们提出了一种解决这一挑战的方法：用于在移动UE和MEC服务器之间进行中间表示（IR）的客户服务器实现，而不是视频编解码器，并且通过这种方式避免了视频解码。我们证明了在边缘计算图形的边缘计算工作负载上解决延迟瓶颈的能力。我们选择兼容兼容的GPU内核作为中间表示。我们的方法需要在GPU体系结构和GPU域特异性语言（DSL）中进行专业知识，但是与基于视频的边缘图形相比，它将UE设备延迟延迟七倍。此外，我们发现，由于UES和MEC服务器上的冷启动时间较低，因此应用迁移可以以毫秒为单位。我们暗示，基于图形的位置感知应用程序（例如MR）可以从这种方法中受益。
Mixed reality (MR) applications are expected to become common when 5G goes mainstream. However, the latency requirements are challenging to meet due to the resources required by video-based remoting of graphics, that is, decoding video codecs. We propose an approach towards tackling this challenge: a client-server implementation for transacting intermediate representation (IR) between a mobile UE and a MEC server instead of video codecs and this way avoiding video decoding. We demonstrate the ability to address latency bottlenecks on edge computing workloads that transact graphics. We select SPIR-V compatible GPU kernels as the intermediate representation. Our approach requires know-how in GPU architecture and GPU domain-specific languages (DSLs), but compared to video-based edge graphics, it decreases UE device delay by sevenfold. Further, we find that due to low cold-start times on both UEs and MEC servers, application migration can happen in milliseconds. We imply that graphics-based location-aware applications, such as MR, can benefit from this kind of approach.
