Name
Abstract | ||
|---|---|---|
The rise of multicore architectures across all computing domains has opened the door to heterogeneous multiprocessors, where processors of different compute characteristics can be combined to effectively boost the performance per watt of different application kernels. GPUs, in particular, are becoming very popular for speeding up compute-intensive kernels of scientific, imaging, and simulation applications. New programming models that facilitate parallel processing on heterogeneous systems containing GPUs are spreading rapidly in the computing community. By leveraging these investments, the developers of other accelerators have an opportunity to significantly reduce the programming effort by supporting those accelerator models already gaining popularity. In this work, we adapt one such language, the CUDA programming model, into a new FPGA design flow called FCUDA, which efficiently maps the coarse- and fine-grained parallelism exposed in CUDA onto the reconfigurable fabric. Our CUDA-to-FPGA flow employs AutoPilot, an advanced high-level synthesis tool (available from Xilinx) which enables high-abstraction FPGA programming. FCUDA is based on a source-to-source compilation that transforms the SIMT (Single Instruction, Multiple Thread) CUDA code into task-level parallel C code for AutoPilot. We describe the details of our CUDA-to-FPGA flow and demonstrate the highly competitive performance of the resulting customized FPGA multicore accelerators. To the best of our knowledge, this is the first CUDA-to-FPGA flow to demonstrate the applicability and potential advantage of using the CUDA programming model for high-performance computing in FPGAs. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2514641.2514652 | ACM Trans. Embedded Comput. Syst. |
Keywords | Field | DocType |
new programming model,cuda code,high-abstraction fpga programming,cuda kernel,high-performance computing,efficient compilation,new fpga design flow,fpga multicore accelerator,cuda programming model,computing domain,computing community,programming effort,cuda-to-fpga flow,high level synthesis,high performance computing,parallel programming model,fpga | Computer architecture,Supercomputer,Programming paradigm,Computer science,CUDA,High-level synthesis,Parallel computing,Field-programmable gate array,Real-time computing,Parallel programming model,Performance per watt,Multi-core processor | Journal |
Volume | Issue | ISSN |
13 | 2 | 1539-9087 |
Citations | PageRank | References |
16 | 0.72 | 14 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandros Papakonstantinou | 1 | 83 | 6.41 |
| Karthik Gururaj | 2 | 177 | 12.19 |
| john a stratton | 3 | 489 | 36.44 |
| Deming Chen | 4 | 1432 | 127.66 |
| Jason Cong | 5 | 1027 | 87.55 |
| Wen-mei W. Hwu | 6 | 4322 | 511.62 |