Heterogeneous computing system and method based on CPU+GPU+FPGA architecture

Abstract

The invention provides a heterogeneous computing system based on CPU+GPU+FPGA architecture. The system comprises a CPU host unit, one or more GPU heterogeneous acceleration units and one or more FPGA heterogeneous acceleration units. The CPU host unit is in communication connection with the GPU heterogeneous acceleration units and the FPGA heterogeneous acceleration units. The CPU host unit is used for managing resources and allocating processing tasks to the GPU heterogeneous acceleration units and / or the FPGA heterogeneous acceleration units. The GPU heterogeneous acceleration units are used for carrying out parallel processing on tasks from the CPU host unit. The FPGA heterogeneous acceleration units are used for carrying out serial or parallel processing on the tasks from the CPU host unit. According to the heterogeneous computing system provided by the invention, the control advantages of the CPU, the parallel processing advantages of the GPU, the performance and power consumption ratio and flexible configuration advantages of the FPGA can be exerted fully, the heterogeneous computing system can adapt to different application scenes and can satisfy different kinds of task demands. The invention also provides a heterogeneous computing method based on the CPU+GPU+FPGA architecture.

Description

technical field [0001] The invention relates to the technical field of heterogeneous computing, in particular to a heterogeneous computing system and method based on CPU+GPU+FPGA architecture. Background technique [0002] The rapid increase in the amount of information in modern society has put forward higher requirements for the computing performance of computers. By vertically improving the processing performance of CPU, we have encountered technical bottlenecks such as manufacturing process and power consumption. The core architecture of the CPU determines that it is better at dealing with irregular data structures and unpredictable access patterns, as well as recursive algorithms, branch-intensive codes, and single-threaded programs. The internal architecture of GPU and FPGA determines that they have strong parallel processing capabilities, making them better at processing computationally intensive tasks. [0003] In the prior art, although GPU can effectively improve ...

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Problems solved by technology

[0003] In the prior art, although GPU can effectively improve computing performance, there is a problem of high power consumption. FPGA can effectively reduce power consumption and has a high performance-to-power ratio, but the peak performance of FPGA is inferior to that of GPU.

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