Об этом курсе

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Английский

Субтитры: Английский

Приобретаемые навыки

Computer ArchitectureComputer ProgrammingConfiguring FPGA systems

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Предполагаемая нагрузка: 4-10 hours/week...

Английский

Субтитры: Английский

Программа курса: что вы изучите

Неделя
1
2 ч. на завершение

Reconfigurable cloud infrastructure

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....
8 видео ((всего 46 мин.)), 3 тестов
8 видео
An overview of cloud infrastructure6мин
Cloud Computing: few definitions7мин
Reconfigurable acceleration in the Cloud4мин
Reconfigurable acceleration in the Cloud: intel FPGA-based solutions6мин
Reconfigurable acceleration in the Cloud: Xilinx FPGA-based solutions5мин
Reconfigurable acceleration in the Cloud: from the past, to the future3мин
An introduction to the AWS EC2 F1 instances7мин
3 практического упражнения
QUIZ 130мин
QUIZ 230мин
QUIZ 35мин
Неделя
2
2 ч. на завершение

On how to accelerate the cloud with SDAccel

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....
9 видео ((всего 51 мин.)), 3 тестов
9 видео
F1: instances and FPGA description3мин
How FPGA Acceleration Works on AWS3мин
AWS F1 Platform Model9мин
Creating Kernels from RTL IP, C/C++, OpenCL6мин
Compiling the Platform3мин
Creating an Amazon FPGA Image2мин
Developing and Executing a Host Application on F17мин
Start Accelerating4мин
3 практического упражнения
QUIZ 410мин
QUIZ 530мин
QUIZ 630мин
Неделя
3
3 ч. на завершение

Summing things up: the Smith-Waterman algorithm

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....
8 видео ((всего 48 мин.)), 1 материал для самостоятельного изучения, 1 тест
8 видео
Algorithm and code analysis5мин
Roofline model 1/26мин
Roofline model 2/24мин
Code profiling6мин
Static Code Analysis 1/26мин
Static Code Analysis 2/24мин
Performance Prediction via Roofline Model7мин
1 материал для самостоятельного изучения
SDAccel Environment Profiling and Optimisation Guide30мин
1 практическое упражнение
QUIZ 730мин
Неделя
4
5 ч. на завершение

The Smith-Waterman example in details

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....
12 видео ((всего 95 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
12 видео
A first implementation 2/39мин
A first implementation 3/34мин
Parallelism in the Smith-Waterman Algorithm8мин
Systolic Array Architecture 1/29мин
Systolic Array Architecture 2/212мин
Input Compression6мин
Shift Register8мин
Dual Physical Ports5мин
Smith-Waterman accelerated on the Amazon EC2 F1 instances 1/36мин
Smith-Waterman accelerated on the Amazon EC2 F1 instances 2/38мин
Smith-Waterman accelerated on the Amazon EC2 F1 instances 3/39мин
2 материала для самостоятельного изучения
Sources Codes30мин
Source Codes30мин
2 практического упражнения
QUIZ 830мин
QUIZ 920мин
1 ч. на завершение

Course conclusions

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....
1 видео ((всего 3 мин.)), 1 материал для самостоятельного изучения
1 материал для самостоятельного изучения
Architectural optimizations for high performance and energy efficient Smith-Waterman implementation on FPGAs using OpenCL45мин

Преподаватели

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Marco Domenico Santambrogio

Associate Professor
DEIB - Dept. of Electronics, Information and Bioengineering

О Politecnico di Milano

Politecnico di Milano is a scientific-technological University, which trains engineers, architects and industrial designers. From 2014 Politecnico di Milano started the release of several MOOCs, developed by the service for digital learning METID (Methods and Innovative Technologies for Learning), giving everybody the chance to enhance personal skills....

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