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我正在对美国所有 3000 多个县的 COVID-19 数据进行线性回归，并且代码运行速度很慢。是否有并行化的选项？

我试过furrr::future_map()了，但它并没有真正加快这个过程。无论有无，CPU 使用率约为 26%，furrr:future_map并且只有一个进程在运行。

示例代码：

library(furrr)
future::plan(multisession, workers = 6)
# also tried multisession workers = 6, (runtime 8.5 min)
# also tried multicore workers = 6, (runtime 3.5 min)
# also tried multicore w/ default workers, (runtime 5.5 min)

# the other called regression functions look very similar
casesmodel <- function(tbl) {
        lm(casesper100k ~ time, data = tbl)
}

uscases_twoweeks <-
    casesdeaths %>%
        filter(date >= twoweeksago) %>%
        filter(!is.na(population)) %>%
        filter(population > min_country_population) %>%
        mutate(countyid = paste(county, state, sep = ", ")) %>%
        arrange(countyid, date) %>%
        group_by(countyid) %>%
        nest() %>%
        mutate(deathmodel = future_map(data, deathsmodel),
               casemodel = future_map(data, casesmodel),
               absdeathmodel = future_map(data, absdeathsmodel),
               abscasemodel = future_map(data, abscasesmodel),
               )
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