XPS:CLCCA:Collaborative Research:Harnessing Highly Threaded Hardware for Server Workloads

数据中心提供了计算和存储基础设施，以满足当今对基于internet的服务不断增长的需求。Web服务器按需提供大量信息，从静态内容（如文件、图像、视频和音频流服务）到通过脚本语言创建的动态内容(如。PHP)或独立的C/ c++应用程序（例如，搜索结果）。在当今基于服务器的行业中，服务器性能、可扩展性和能源效率（吞吐量/瓦特）是降低总拥有成本（TCO）的关键因素。不幸的是，当前基于商用多核处理器的系统设计可能不是对所有服务器工作负载最节能的。今天的大规模并行加速器（例如gpu）与传统的多核cpu相比，在某些工作负载下提供了卓越的每瓦特性能。不幸的是，这些设备在高性能计算领域之外还没有找到广泛的通用用途。该项目通过跨应用程序、运行时、操作系统和体系结构层的创新，将大规模并行加速器的使用扩展到服务器和操作系统密集型工作负载。这项研究建立在服务器工作负载执行模式在多个请求中并非完全唯一的观察基础之上。这个项目的目标是开发计算机系统（软件和硬件），利用请求之间的相似性，通过为请求队列启动数据并行执行来提高服务器性能和电源/能源效率。本研究的三个主要方面是：1)将传统的线程/任务并行工作负载映射到数据并行硬件上；2)开发新的以加速器为中心的操作系统体系结构；3)开发新的体系结构机制来支持这类新的加速器工作负载和操作系统软件。

Data centers provide the computational and storage infrastructure required to meet today's ever increasing demand for Internet-based services. Web servers deliver a vast range of information on demand, ranging from static content such as files, images, video and audio streaming services, to dynamic content created via scripting languages (e.g,. PHP) or stand alone C/C++ applications (e.g., search results). Server performance, scaling and energy efficiency (throughput/Watt) are crucial factors in reducing total cost of ownership (TCO) in today's server-based industries. Unfortunately, current system designs based on commodity multicore processors may not be the most power/energy efficient for all server workloads. Today's massively parallel accelerators (e.g., GPUs) provide exceptional performance per Watt for certain workloads versus conventional many core CPUs. Unfortunately, these devices have not found wide-spread general purpose use outside the high-performance computing domain. This project expands the use of massively parallel accelerators to server and operating system-intensive workloads by innovating across the application, runtime, operating system, and architecture layers.This research builds on the observation that server workload execution patterns are not completely unique across multiple requests. The goal of this project is to develop computer systems (software and hardware) that exploit similarity across requests to improve server performance and power/energy efficiency by launching data parallel executions for request cohorts. The three primary aspects of this research are 1) mapping traditional thread/task parallel workloads onto data parallel hardware, 2) developing a new accelerator-centric operating system architecture, and 3) developing new architectural mechanisms to support this new class of accelerator workloads and operating system software.