Learn how to develop new applications written in OpenCL, C/C++, and RTL in the SDAccel™ development environment for use on Xilinx FPGAs. Porting existing applications is also covered.
This course also demonstrates how to debug and profile OpenCL code using the SDAccel development environment. In addition, you will also learn how to maximize performance and efficiently utilize FPGA resources.
Who should attend?Software and hardware developers who want to develop OpenCL, C/C++, and RTL applications in the SDAccel development environment.
• Basic knowledge of C/C++
• SDAccel development environment and common build tools
Skills Gained: After completing this training, you will be able to:
• Identify parallel computing applications suitable for accelerating on FPGAs
• Discover how the FPGA architecture lends itself to parallel computing
• Write OpenCL programs for FPGAs
• Examine the OpenCL execution model
• Analyze the OpenCL memory model
• Profile and debug OpenCL code using the SDAccel development environment
• Discover how to maximize performance in FPGA fabric
• Efficiently utilize FPGA memory resources
• Utilize the SDAccel development environment
• Rapidly develop FPGA applications using OpenCL
• Port programs written in OpenCL for CPUs or GPUs to Xilinx FPGAs
Lab 1: Creating an OpenCL Program from Scratch
Creating an OpenCL Program from Scratch – Provides an overview of OpenCL API, memory transfers, and kernel enqueuer operations.
Lab 2: Vector Addition
Vector Addition – Learn how to execute parallel kernels.
Lab 3: Pi by Monte Carlo Processes
Pi by Monte Carlo Processes – Implement the Pi by Monte Carlo processes.
Lab 4: Maximizing Performance
Maximizing Performance – Use vector data types and increase bandwidth.
Lab 5: Optimizing Kernels
Optimizing Kernels – Use Loop Unrolling and Loop Pipelining.
Lab 6: Profiling and Debugging Using the SDAccel Development Environment GUI
Profiling and Debugging Using the SDAccel Development Environment GUI – Learn how to use interactive programming tools to improve performance and squash bugs.
Lab 7: Optimizing C/C++ Code for OpenCL
Optimizing C/C++ Code for OpenCL – Convert existing C/C++ code into a kernel that can be used by OpenCL
Lab 8: Using an RTL Kernel
Using an RTL Kernel – Learn how to use existing, highly optimized IP in a new OpenCL application.
1. Introduction to OpenCL
2. Comparison of CPU, GPU, and FPGA Architectures
3. OpenCL Support for Xilinx FPGAs
4. FPGA Hardware Details
5. Introduction to the OpenCL API
6. OpenCL Execution Model
7. Memory Hierarchy
8. Profiling and Debugging
10. Using the SDAccel Development Environment: Coding, Compiling, Emulating, Profiling, and Debugging
11. Using Existing C/C++ Code as Kernels in OpenCL
12. RTL IP as Kernels in OpenCL