pyro: MCMC does not work well with multi-chains in CPU due to Memory Error

The following script is copied exactly from Bayesian regression tutorial (except num_samples=3000 and num_chains=2). While running it, I get MemoryError. I have tried to debug this memory error by changing various settings (ulimit, shared memory segment) in my system but no hope. I get this issue in two systems which I have so I guess this will not happen for only me. If so, then this is a serious problem which can come from how we use torch.multiprocessing in Pyro.

import numpy as np
import pandas as pd
import torch

import pyro
import pyro.distributions as dist
from pyro.infer.mcmc import MCMC, NUTS

pyro.enable_validation(True)
pyro.set_rng_seed(1)
DATA_URL = "https://d2fefpcigoriu7.cloudfront.net/datasets/rugged_data.csv"
rugged_data = pd.read_csv(DATA_URL, encoding="ISO-8859-1")

#torch.multiprocessing.set_sharing_strategy("file_system")


def model(is_cont_africa, ruggedness, log_gdp):
    a = pyro.sample("a", dist.Normal(8., 1000.))
    b_a = pyro.sample("bA", dist.Normal(0., 1.))
    b_r = pyro.sample("bR", dist.Normal(0., 1.))
    b_ar = pyro.sample("bAR", dist.Normal(0., 1.))
    sigma = pyro.sample("sigma", dist.Uniform(0., 10.))
    mean = a + b_a * is_cont_africa + b_r * ruggedness + b_ar * is_cont_africa * ruggedness
    with pyro.iarange("data", len(ruggedness)):
        pyro.sample("obs", dist.Normal(mean, sigma), obs=log_gdp)

        
df = rugged_data[["cont_africa", "rugged", "rgdppc_2000"]]
df = df[np.isfinite(df.rgdppc_2000)]
df["rgdppc_2000"] = np.log(df["rgdppc_2000"])
train = torch.tensor(df.values, dtype=torch.float)
is_cont_africa, ruggedness, log_gdp = train[:, 0], train[:, 1], train[:, 2]

nuts_kernel = NUTS(model, adapt_step_size=True)
hmc_posterior = MCMC(nuts_kernel, num_samples=4000, warmup_steps=1000, num_chains=4).run(is_cont_africa, ruggedness, log_gdp)

Here is its backtrack