Heterogeneous accelerating genetic calculating method based on FPGA in cloud platform

Abstract

The invention discloses a heterogeneous accelerating genetic calculating method based on FPGA in a cloud platform. By the calculation method, genetic calculation is divided into a slow calculation part in a computer processor and a fast calculation part in the computer processor, hardware is adopted to accelerate according to the slow calculation part, software is adopted to accelerate according to the fast calculation part, the software accelerating part is achieved by the cloud platform, the hardware accelerating part is achieved by being downloaded to the FPGA provided by the cloud platform, and finally the acceleration is achieved by synthesizing results of heterogeneous acceleration. According to the method, heterogeneous calculation used by both a universal calculating platform and the FPGA is adopted, compared with a single processing unit, the FPGA can achieve the optimal combination of a hardware structure and calculation by changing the connecting relations of the hardware, and the speed is faster; compared with calculation which only uses the FPGA, the heterogeneous calculation enables processing units of two structures to exert respective strengths and has a better effect compared with the single processing unit, data quantity of single transmission is increased after reinforcing communication interfaces, transmission times are reduced, and the integral acceleration effect is improved.

Description

technical field [0001] The invention relates to the field of biological information computing, in particular to an FPGA-based heterogeneous accelerated genetic computing method in a cloud platform. Background technique [0002] Parallel computing performed on heterogeneous computing systems is often referred to as heterogeneous computing. Heterogeneous computing has a wide range of applications, and almost all solutions to hugely challenging problems can be solved cost-effectively with heterogeneous computing. Typical applications include image understanding, particle tracing, acoustic beamforming, climate modeling, turbulent convective mixing simulations, and multimedia queries. These applications usually contain a variety of different computing types, so they are very suitable for solving with heterogeneous computing. [0003] The rapid growth of biological information and the continuous decline of data output costs will inevitably bring more data processing requirements...

AI Technical Summary

Problems solved by technology

However, simply applying the FPGA heterogeneous accelerated computing system to genetic computing cannot meet the needs of application feature diversity

Purely based on the FPGA heterogeneous acceleration system, the main control processor and FPGA with fixed functions are generally used for calculation, especially the hardware acceleration program in the FPGA, so the task allocation is extremely inflexible, and the software and hardware cannot be automatically performed according to the different genetic calculation programs. Division, which greatly reduces the computational efficiency

Images