This course, which is the fourth one of a “series”, is for anyone passionate about practicing how to develop FPGA-accelerated applications with SDAccel. 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 they need to process and the complexity of these algorithms, raised the problem of increasing the amount of computational power needed to perform the computation. In this scenario, hardware accelerators revealed to be efficient 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 presented 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.
Prerequisites
This course follows the previous ones “FPGA computing systems: A Bird’s Eye View on Reconfigurable Computing”, “FPGA computing systems: Partial Dynamic Reconfiguration” and “Developing FPGA-accelerated cloud applications with SDAccel: Theory”. Within this context no specific background knowledge is requested. Anyone with moderate computer experience should be able to master the materials in this course.
