{'fraction_non_alphanumeric': 0.03814262023217247, 'fraction_numerical': 0.003316749585406302, 'mean_word_length': 5.18974358974359, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Co-mining: a processing-in-memory assisted framework for memory-intensive PoW acceleration

Recently, HBM (High Bandwidth Memory) and PIM (Processing in Memory) integrated technology such as Samsung function-in-memory DRAM opens a new door for memory-intensive PoW acceleration by jointly exploiting GPU, PIM and HBM. In this paper, we for the first time propose a GPU/PIM Co-Mining framework to accelerate memory intensive PoW by fully exploiting HBM-PIM's bandwidth and coordinately scheduling mining tasks in both GPU and PIM. Specifically, we first design a linear programming model to intelligently guide the GPU/PIM task scheduling. An extended finite-state-machine model is designed for the GPU memory controller to switch PIM working mode (compute/memory mode) accordingly. Finally, considering the speed difference between intra-/inter-channel memory accesses, a hybrid memory access method is proposed to minimize inter-channel data movements. We evaluate Co-Mining based on Samsung's HBM2-based function-in-memory architecture. The experimental results show that it can achieve up to 38.5% hashrate improvement compared with the method by directly integrating PIM into PoW acceleration with GPU.