Pandas read_csv with JIT Bodo is slower than regular Python

I’m trying out Bodo to speed up certain Pandas operations, the first being pd.read_csv(...). Bodo requires the compatible pandas code to be in a separate function, separate from non-Bodo compatible code. For example, this is my code:

With Bodo:

import bodo

@bodo.jit
def loadDataFileWithJIT(filePath):
    df = pd.read_csv(filePath, header=0, sep="t", names=["patid", "eventdate", "prodcode", "consid", "issueseq"],
                       usecols=[0, 1, 3, 4, 12],
                       dtype={"patid": "str", "eventdate": "str", "prodcode": "str", "consid": "str", "issueseq": "str"},
                       low_memory=False)
    return df

Over 5 files I see these times:

• 14.24 <— first time, so this is when JIT compiles
• 9.67
• 10.72
• 9.51
• 9.42

Without Bodo (the function decorator and import statement have been removed… nothing else has changed):

• 4.66
• 4.68
• 4.59
• 4.61
• 4.60

Each file is approximately 170MB.

Update

Having spoken with the authors of Bodo I need to be running Python from mpiexec -n # (where # is number of cores > 1) if I’m to see a speed up.