LCA and LSTM-Based Predictions of Environmental Impact in HPC

The growing demand for high-performance computing (HPC) is exacerbating its pollution of the environment, so we need to explore environmentally friendly and sustainable solutions. By collecting global energy consumption data for HPC systems, we have analyzed the energy mix and its impact on consumption to understand the environmental impact of HPC better and build a global carbon emissions model based on it. The model quantifies the ecological effects of HPC by integrating the carbon footprint of various energy mixes. At the same time, we predicted energy consumption in 2030 using a Long Short Term Memory (LSTM) network. We also simulate the impact of increasing renewable energy on reducing carbon emissions. Incorporating renewable energy policies into our model allows us to simulate their effect on carbon emissions and energy consumption and assess the potential environmental benefits. However, the practical application of 100 percent renewable energy is still challenging. In addition, we comprehensively evaluated the impact of HPC on other environmental factors. Based on this, we recommend a combination of technological and policy measures.