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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/fpga-sdaccel-practice
课程评论：没有评论
课程名称：使用SDAccel开发FPGA加速的云应用：实践 课程概述：本课程面向任何热衷于学习如何使用SDAccel开发FPGA加速应用的人士。随着需求的增加，传统的CPU在性能上的局限日益显现，因此云和数据中心的架构正向加速计算转型。本课程将展示如何通过使用Xilinx SDAccel编程Amazon EC2 F1实例，从而获得加速性能，尤其是在生物计算领域的算法应用。通过探讨Smith-Waterman算法，学生将了解如何对DNA序列进行成对比对，并进行FPGA硬件加速的分析与实现。 课程大纲： 1. **可重构云基础设施** 介绍分布式系统、数据中心和云架构面临的计算需求急剧增长，以及基于CPU的解决方案的局限性，强调加速器在提供性能和功率效率上的重要性。 2. **如何用SDAccel加速云计算** 讲解如何结合F1实例与SDAccel开发环境来开发加速应用，重点介绍创建FPGA内核、组装FPGA程序和编译亚马逊FPGA映像（AFI）的必要步骤。 3. **总结：Smith-Waterman算法** 介绍Smith-Waterman算法的详细信息，包括数据结构和计算流程，并使用Roofline模型分析其理论峰值性能和操作强度。 4. **Smith-Waterman示例详细解析** 深入实现初步算法版本，探索性能优化技术，例如引入并行处理和使用系统流数组，并研究数据压缩及多内存端口的应用，最终将实现移植到AWS F1实例。 5. **课程总结** 探讨FPGA技术在云基础设施中的应用，讨论加速后端计算和云服务的研究机遇，并为学生指向未来可能的研究方向和其他相关课程提供建议。 本课程将帮助学生掌握如何利用现代加速技术，实现高效的云计算解决方案。
Name:Reconfigurable cloud infrastructure
Description:Distributed systems, data center and cloud architectures are facing the exponential growth in computing requirements and the impossibility for CPU-based solutions to keep pace. Within this context these complex distributed systems have to move toward accelerated computing. Accelerators complement CPU-based architectures and deliver both performance and power efficiency. Moreover, modern data center, as we know, can be used by several different users to serve different workloads and the idea of having an underlying architecture built on reconfigurable technologies seems to provide an ideal fit for these changing, demanding, workloads. This module provides a description of the main cloud computing components and technologies, as well as detailing the current technologies to accelerate cloud computing workloads.
Name:On how to accelerate the cloud with SDAccel
Description:Within this module we are going to have a first taste on how to gain the best out of the combination of the F1 instances with SDAccel providing some few practical instructions on how to develop accelerated applications on Amazon F1 by using the Xilinx SDAccel development environment. Then, we are going to present what it is necessary to create FPGA kernels, assemble the FPGA program and to compile the Amazon FPGA Image, or AFI. Finally, we will describe the steps and tasks involved in developing a host application accelerated on the F1 FPGA.
Name:Summing things up: the Smith-Waterman algorithm
Description:Within this module we are going to introduce you to the Smith-Waterman algorithm that we have chosen to demonstrate how to create a hardware implementation of a system based on FPGA technologies using the Xilinx SDAccel design framework. We are going to dig into the details of the algorithm from its data structures to the computation flow. Then we are going to introduce the Roofline model and we are going to use it to analyze the theoretical peak performance and the operational intensity of the Smith-Waterman algorithm.
Name:The Smith-Waterman example in details
Description:Within this module we are going to dig deeper in the Smith-Waterman algorithm. We are going to implement a first version of the algorithm on a local server with the Xilinx SDAccel design framework. Then we are going to introduce some optimizations to improve performance, in particular we will add more parallelism in the implementation and we will introduce systolic arrays. Moreover, we will explore how we can perform data compression and then we will leverage multiple memory ports to improve memory access speed. Finally, we are going to port our implementation of the Smith-Waterman algorithm on the AWS F1 instances.
Name:Course conclusions
Description:We are working at the edge of the research in the area of reconfigurable computing. FPGA technologies are not used only as standalone solutions/platforms but are now included into cloud infrastructures. They are now used both to accelerate infrastructure/backend computations and exposed as-a-Service that can be used by anyone. Within this context we are facing the definition of new research opportunities and technologies improvements and the time cannot be better under this perspective. This module is concluding this course but posing interesting questions towards possible future research directions that may also point the students to other Coursera courses on FPGAs.
This course is for anyone passionate about learning how to develop FPGA-accelerated applications with SDAccel! The more general purpose you are, the more flexible you are and the more kinds of programs and algorithms you can execute on your underlying computing infrastructure. All of this is terrific, but there is no free food and this is happening, quite often, by losing in efficiency. This course will present several scenarios where the workloads require more performance than can be obtained even by using the fastest CPUs. This scenario is turning cloud and data center architectures toward accelerated computing. Within this course, we are going to show you how to gain benefits by using Xilinx SDAccel to program Amazon EC2 F1 instances. We are going to do this through a working example of an algorithm used in computational biology. The huge amount of data the algorithms need to process and their complexity raised the problem of increasing the amount of computational power needed to perform the computation. In this scenario, hardware accelerators revealed to be effective in achieving a speed-up in the computation while, at the same time, saving power consumption. Among the algorithms used in computational biology, the Smith-Waterman algorithm is a dynamic programming algorithm, guaranteed to find the optimal local alignment between two strings that could be nucleotides or proteins. In the following classes, we present an analysis and successive FPGA-based hardware acceleration of the Smith-Waterman algorithm used to perform pairwise alignment of DNA sequences. Within this context, this course is focusing on distributed, heterogeneous cloud infrastructures, providing you details on how to use Xilinx SDAccel, through working examples, to bring your solutions to life by using the Amazon EC2 F1 instances.
