read_csv cannot get big files
#### Code Sample, a copy-pastable example if possible

```python
single_df = pd.read_csv(f, sep="\t", header=0, index_col=0)

```
#### Problem description

I am trying to read a 5GB tsv file into RAM, I reserve 100GB on the computational cluster I am working on and the process still gets Killed. It there a workaround for reading big files?

#### Expected Output

#### Output of ``pd.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-642.6.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US

pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 21.0.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: None
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
None
</details>