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Modify readme makefile #1

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7333909
small modification to the Makefile
tammyyang Sep 23, 2015
10d16f0
modify README.md to add guidelines for building and installation
tammyyang Sep 23, 2015
6d7554c
fix a minor bug in README.md
tammyyang Sep 23, 2015
a4891fe
use bullets instead of lists
tammyyang Sep 23, 2015
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6 changes: 3 additions & 3 deletions Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -5,11 +5,11 @@ all: translator pylib

translator: cabal_hack
# Build
cd ${translator} && cabal configure --enable-shared && cabal build
cd ${translator} && cabal configure --enable-shared && cabal build -v2

# Copy binary files
mkdir -p bin
# cp ${translator}/dist/build/libHS${translator}*.a bin/${translator}.a
cp ${translator}/dist/build/libHS${translator}*.dylib bin/${translator}.so
cp ${translator}/dist/build/libHS${translator}*.so bin/${translator}.so

cabal_hack:
# We have to manually link the runtime library in cabal for some reason.
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23 changes: 23 additions & 0 deletions README.md
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Expand Up @@ -218,6 +218,29 @@ Here is the Mandelbrot image:
![Mandel](http://418.oulgen.com/img/mandel.png)


# Building and Installing

* The suggested version of ghc is 7.8.3, you can find it from [here](https://www.haskell.org/ghc/download).
* Make sure the external dependencies are satisfied
* For Ubuntu 14.04 and later versions
```{r, engine='bash', count_lines}
sudo apt-get install cabal-install happy alex haskell-platform ghc-dynamic python-setuptools
```
* Make sure the dependencies are installed properly using [cabal](https://wiki.haskell.org/Cabal-Install)
```{r, engine='bash', count_lines}
cd py2cuda
cabal install --only-dependencies
cd -
```
* Build it!
* The resulting Python egg file will be placed in bin/. Install them properly or add the following three lines to your Python codes if you just want to make some test:
```python
import sys
sys.path.append("$PATH_OF_CudaPy/bin/cudapy.egg")
import cudapy
```


# Related Work

We got our inspiration for CudaPy form a system called VecPy. VecPy was last year's winner at [15-418 Parallelism competition](http://15418.courses.cs.cmu.edu/spring2014/competition). As in its creator's words, VecPy "leverages multi-threading and SIMD instructions on modern x86 processors." CudaPy goes in a different direction and adds GPU level parallelism. We also have a less strict type system: VecPy compiles code for a single type like `int` or `float` where as CudaPy kernels can take an arbitrary signature of base types (these include `void`, `bool`, `int`, `float`, `double`, and possibly nested arrays on these types). Finally, CudaPy had some extra challenges VecPy did not have such as handling separate device and host memory, and interfacing with the CUDA runtime.
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