Developing FPGA-accelerated cloud applications with SDAccel: Practice

Go to Course: https://www.coursera.org/learn/fpga-sdaccel-practice

Introduction

**Course Review: Developing FPGA-accelerated Cloud Applications with SDAccel: Practice** If you're delving into the advanced realms of computing and seeking to harness the power of FPGA (Field-Programmable Gate Array) technology to accelerate cloud applications, Coursera’s course, "Developing FPGA-accelerated Cloud Applications with SDAccel: Practice," is a commendable choice. This course caters to both beginners and those with some background in computing, enabling you to evolve your skills in a growing field that is becoming more relevant as cloud infrastructures demand higher performance solutions. ### Course Overview The course focuses on teaching the development of FPGA-accelerated applications using the Xilinx SDAccel development environment. It provides an immersive experience in understanding reconfigurable cloud infrastructures and how they can be utilized to optimize performance for diverse workloads. The instructors delve into how traditional CPU-based solutions often lag behind the requirements of contemporary distributed systems. Thus, acquiring knowledge on accelerated computing technologies becomes essential for anyone eager to stay ahead in the tech domain. ### Syllabus Breakdown 1. **Reconfigurable Cloud Infrastructure**: - In this module, students are introduced to the challenges posed by increased computing demands in distributed systems and data centers. The implementation of reconfigurable technologies is discussed as a practical approach to meet these demands efficiently. The depth of information presented here lays a strong foundation for understanding the context in which FPGAs operate within modern cloud architectures. 2. **Accelerating the Cloud with SDAccel**: - This section gives a hands-on introduction to the use of AWS F1 instances with SDAccel. Students will gain practical insights into developing hardware-accelerated applications, creating FPGA kernels, and compiling the Amazon FPGA Image (AFI). The real-world application of these practices makes this module particularly engaging. 3. **Understanding the Smith-Waterman Algorithm**: - Here, you will explore a classic algorithm that showcases how FPGA technology can be leveraged. The course unpacks the data structures and computation flow, thus providing a solid understanding of both the algorithm and performance metrics through the Roofline model. This module effectively bridges theoretical concepts and practical implementation. 4. **Detailed Implementation of the Smith-Waterman Algorithm**: - Moving beyond the foundational knowledge, this module emphasizes enhancing the algorithm’s performance through various optimizations. Students will learn to implement parallelism, design systolic arrays, utilize data compression, and increase memory access efficiency. Transitioning your implementation to the AWS F1 instances provides a valuable experience, integrating everything into a cloud context. 5. **Course Conclusions and Future Directions**: - As the course wraps up, students are encouraged to reflect on their learning and explore future research opportunities in the FPGA domain. The instructors deftly connect current course content to potential advancements and other resources available on Coursera, fostering continuous learning. ### Recommendation I highly recommend "Developing FPGA-accelerated Cloud Applications with SDAccel: Practice" for anyone passionate about cloud computing and reconfigurable technologies. The course’s expertly crafted modules not only deliver theoretical knowledge but also immerse students in applied learning, crucial for mastering FPGA-accelerated solutions. As cloud technology continues to evolve, the competencies you develop through this course will be invaluable, opening paths to innovative projects and research in high-performance computing. Whether you're a student, a tech enthusiast, or a professional seeking to upscale your skills, this course represents an essential building block towards becoming proficient in the field of FPGA applications in the cloud. ### Final Thoughts Investing your time in this course could pave the way for significant career advancements in tech industries where efficiency, speed, and adaptability are prized. Join the community of forward thinkers and innovators as you embark on this rewarding educational journey on Coursera.

Syllabus

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.

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.

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.

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.

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.