Introduction

To achieve the highest possible acceleration of a software application, recent advances have included the development of multi-core and heterogeneous computing platforms. These architectures enable the software engineer to more effectively trade-off performance and power for different form factors and computational loads. The one challenge in using these new computing architectures is the programming model of each platform. All multi-core and heterogeneous computing platforms require the programmer to rethink the problem to be solved in terms of explicit parallelism.

Recognizing the programming challenge of multi-core and heterogeneous compute platforms, the Khronos™ Group industry consortium has developed the OpenCL™ programming standard. The OpenCL specification for multi-core and heterogeneous compute platforms defines a single consistent programming model and system-level abstraction for all hardware platforms that support the standard. This means that a software engineer learns a single programming model and directly uses it on devices from multiple vendors.

Xilinx is an active member of the Khronos Group, collaborating on the specification of OpenCL, and supports the compilation of OpenCL programs for Xilinx FPGAs. SDAccel™ is the Xilinx® development environment for compiling OpenCL programs to execute on Xilinx FPGAs.

The OpenCL standard guarantees functional portability but not performance portability. Therefore, even though the same code will run on every platform supporting OpenCL, the performance achieved will vary depending on coding style and capabilities of the underlying hardware. Optimizing for an FPGA using the SDAccel™ tool chain requires the same effort as code optimization for a CPU/GPU. The one difference in optimization for these platforms is that in a CPU/GPU, the programmer is trying to get the best mapping of an application onto a fixed architecture. For an FPGA, the programmer is concerned with guiding the compiler to generate optimized compute architecture for each accelerator (referred to as a kernel) in the application.

As specified by the OpenCL standard, any code that complies with the OpenCL specification is functionally portable and will execute on any computing platform that supports the standard. Therefore, any code changes are for performance optimization. To aid the user in these optimizations, SDAccel offers performance profiling capabilities integrated into the run-time. This profiling helps the user analyze the achieved performance and pinpoint any potential bottlenecks that need to be addressed.