Enabling virtual environment for multiple LLVM versions on Linux, MacOS, Windows and Android

It's a painful process to continuously switch between different versions of LLVM (including the ones built locally), set up numerous environment variables, and fix cmake when benchmarking and optimizing different AI/ML/math libraries.

Eventually, we decided to automate this process by introducing "virtual environments" for LLVM and related tools in the CK framework similar to Python virtualenv while supporting Linux, MacOS, Windows and Android. It automatically sets all necessary environment variables for
different versions of different tools natively installed on a user machine.

CK virtual environment requires minimal dependencies (just python, pip and git) and you can try it on your machine as follows (you can check CK installation guide if you have issues):

 $ (sudo) pip install ck
 $ ck pull repo:ck-env

 $ ck detect soft --tags=compiler,llvm

CK will search for all installed clang instances on your machine (including Microsoft Visual Studio dependency on Windows) and will ask you which one to register for the CK virtual environment. You can then repeat this process and register multiple versions you use for testing. You can then see all registered virtual environments as follows:

 $ ck show envor
 $ ck show env --tags=compiler,llvm

Now you can start a specific virtual environment as follows:

 $ ck virtual env --tags=compiler,llvm

CK will set up PATH, LD_LIBRARY_PATH and other variables to point to a specific clang version, and will start bash on Linux/MacOS or shell on Windows. You can then use different environment variable specific to a given Clang instance and starting from CK_ in your own scripts:

 $ export | grep "CK_"

We also added the possibility to install pre-built versions of LLVM on different platforms using CK packages and automatically register CK virtual environment:

 $ ck search package --tags=compiler,llvm
 $ ck install package --tags=compiler,llvm,v6.0.0
 $ ck show env --tags=compiler,llvm
 $ ck virtual env --tags=compiler,llvm,v6.0.0
 
  > ${CK_CC_FULL_PATH} --version 
or
  > %CK_CC_FULL_PATH% --version

Finally, you can also rebuild LLVM from a trunk and again automatically register it in the CK virtual environment:

 $ ck install package --tags=compiler,llvm,vtrunk

You can find other software which you can automatically detect and register in the CK virtual environment using CK plugins in this online list.

If you are interested to know more about this and other CK functionality, please check the "CK getting started guide" or feel free to get in touch with the CK community!

ACM ReQuEST: 1st open and reproducible tournament to co-design Pareto-efficient deep learning (speed, accuracy, energy, size, costs)

The first Reproducible Quality-Efficient Systems Tournament (ReQuEST) will debut at ASPLOS’18 ( ACM conference on Architectural Support for Programming Languages and Operating Systems, which is the premier forum for multidisciplinary systems research spanning computer architecture and hardware, programming languages and compilers, operating systems and networking).

Organized by a consortium of leading universities (Washington, Cornell, Toronto, Cambridge, EPFL) and the cTuning foundation, ReQuEST aims to provide a open-source tournament framework, a common experimental methodology and an open repository for continuous evaluation and multi-objective optimization of the quality vs. efficiency Pareto optimality of a wide range of real-world applications, models and libraries across the whole software/hardware stack.

ReQuEST will use the established artifact evaluation methodology together with the Collective Knowledge framework validated at leading ACM/IEEE conferences to reproduce results, display them on a live dashboard and share artifacts with the community. Distinguished entries will be presented at the associated workshop and published in the ACM Digital Library. To win, the results of an entry do not necessarily have to lie on the Pareto frontier, as an entry can be also praised for its originality, reproducibility, adaptability, scalability, portability, ease of use, etc.

The first ReQuEST competition will focus on deep learning for image recognition with an ambitious long-term goal to build a public repository of portable and customizable “plug&play” AI/ML algorithms optimized across diverse data sets, models and platforms from IoT to supercomputers (see live demo). Future competitions will consider other emerging workloads, as suggested by our Industrial Advisory Board.

For more information, please visit http://cKnowledge.org/request