Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-11-29 Cooperative journals: 《计算机应用研究》
Abstract： At present, the traditional method of cracking WinRAR is to use the CPU and GPU, but the potential password space is very large which requiries a higher performance computing platform to find the correct password within a limited time. Therefore, this paper uses the hardware platform of multi-FPGA to achieve a high-performance WinRAR crack algorithm. The SHA-1 algorithm was optimized by adding pre-calculation and carry saving adder under the full-pipeline architecture, so as to improve the throughput of the algorithm. And the use of state machine control to optimize data splicing, to improve algorithm parallelism. At the same time, it used the asynchronous clock and multiple FIFO buffers to read and write the overall structure of the data optimization algorithm to reduce the coupling within the algorithm. The experimental results show that the final optimized resource utilization rate is 75% and the frequency reaches 200MHz. The 4-byte password deciphering speed is 102, 796 per second, which is 100 times faster than CPU and 3.5 times faster than GPU.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-24 Cooperative journals: 《计算机应用研究》
Abstract： In view of the current big data applications mainly use the general processor as the computing core, and the system structure is simple, energy efficiency ratio is low, can’t fully meet the big data computing needs. In this paper, based on mimic computing model, a design method of big data high-efficiency platform is put forward. This method took computing grain as the basic research object, deeply analyzed the features of big data application algorithms, and reasonably divided the computational subtasks. Secondly, an architecture matching matrix was constructed and the subtasks were assigned to the right processing units. Finally, dynamic voltage/frequency scaling technology and data layout algorithm were used to control the voltage of non-critical tasks and optimize the structure layout of critical tasks. The experimental results show that the mimic computing can integrate the heterogeneous computing components in depth, establish a flexible and scalable architecture, give full play to the overall efficiency of the system, reduce the power consumption and improve the energy efficiency ratio.
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