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Thread- and data-level parallel simulation in SystemC, a Bitcoin miner case study

机译:Systemc中的线程和数据级并行模拟,比特币矿工案例研究

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The rapidly growing design complexity has become a big obstacle and dramatically increased the time required for SystemC simulation. In this case study, we exploit different levels of parallelism, including thread- and data-level parallelism, to accelerate the simulation of a Bitcoin miner model in SystemC. Our experiments are performed on two multi-core processors and one many-core Intel(g) Xeon PhiTM Coprocessor. Our results show that with the combination of data- and thread-level parallelism, the peak simulation speed improves by over 11× on a 4-core host, 50× on a 16-core host, and 510× on a 60-core host, respectively. The results confirm the efficiency of combining thread- and datalevel parallelism for higher SystemC simulation speed, and can serve as a benchmark for future optimization of system level design, modeling, and simulation.
机译:快速增长的设计复杂性已成为一个大的障碍,并且大大增加了系统仿真所需的时间。在这种情况下,我们利用不同级别的并行性,包括线程和数据级并行性,以加速Systemc中的比特币矿业模型的模拟。我们的实验是在两个多核处理器和一个多核英特尔(G)Xeon Phi TM 协处理器上进行的。我们的结果表明,随着数据和螺纹级并行性的组合,峰值仿真速度在4核主机上提高了超过11倍,在16核主机上为50倍,50×60芯主机, 分别。结果证实了组合线程和DataLevel Parallelism的效率,以实现更高的系统仿真速度,并且可以作为系统级设计,建模和仿真的未来优化的基准。

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