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Coriander

Build applications written in NVIDIA® CUDA™ code for OpenCL™ 1.2 devices.

Concept

  • leave applications in NVIDIA® CUDA™
  • compile into OpenCL 1.2
  • run on any OpenCL 1.2 GPU

How to use

  • Write an NVIDIA® CUDA™ sourcecode file, or find an existing one
  • Let's use cuda_sample.cu
  • Compile, using cocl:
$ cocl_py cuda_sample.cu
   ...
   ... (bunch of compily stuff) ...
   ...

    ./cuda_sample.cu compiled into ./cuda_sample

Run:

$ ./cuda_sample
Using Intel , OpenCL platform: Intel Gen OCL Driver
Using OpenCL device: Intel(R) HD Graphics 5500 BroadWell U-Processor GT2
hostFloats[2] 123
hostFloats[2] 222
hostFloats[2] 444

Options

What Coriander provides

  • compiler for host-side code, including memory allocation, copy, streams, kernel launches
  • compiler for device-side code, handling templated C++ code, converting it into bog-standard OpenCL 1.2 code
  • cuBLAS API implementations for GEMM, GEMV, SCAL, SAXPY (using Cedric Nugteren's CLBlast)
  • cuDNN API implementations for: convolutions (using im2col algorithm over Cedric Nugteren's CLBlast, pooling, ReLU, tanh, and sigmoid

How Coriander works

Kernel compilation proceeds in two steps:

Slides on the IWOCL website, here

Installation

Coriander development is carried out using the following platforms:

  • Ubuntu 16.04, with:
    • NVIDIA K80 GPU and/or NVIDIA K520 GPU (via aws)
  • Mac Book Pro 4th generation (thank you ASAPP :-) ), with:
    • Intel HD Graphics 530
    • Radeon Pro 450
    • Sierra OS

Other systems should work too, ideally. You will need at a minimum at least one OpenCL-enabled GPU, and appropriate OpenCL drivers installed, for the GPU. Both linux and Mac systems stand a reasonable chance of working ok.

For installation, please see installation

Plugins

You can install the following plugins:

  • Coriander-clblast: just do cocl_plugins.py install --repo-url https://github.com/hughperkins/coriander-clblast
  • Coriander-dnn: just do cocl_plugins.py install --repo-url https://github.com/hughperkins/coriander-dnn
  • Your plugin here?

How to create a plugin

Add to your own cmake project

  • use cocl_add_executable and cocl_add_library
  • see cmake usage

Testing

See testing

Assumptions/relaxations made by Coriander

See assumptions

Roadmap

Libraries

Coriander uses the following libraries:

  • clang/llvm: c/c++ parser/compiler; many contributors
  • thrust: parallel GPU library, from NVIDIA®
  • yaml-cpp: yaml for c++, by Jesse Beder
  • EasyCL: wrapper for OpenCL 1.2 boilerplate
  • argparsecpp: command-line parser for c++
  • gtest: unit tests for c++, from Google

Related projects

How to Cite

Please cite: CUDA-on-CL: a compiler and runtime for running NVIDIA® CUDA™ C++11 applications on OpenCL™ 1.2 Devices

License

Apache 2.0

News

  • June 23:
    • factorized CLBlast implementation of NVIDIA® CUDA™ cuBLAS API, into new plugin coriander-clblast
  • June 21:
    • created a new release v6.0.0, that marks a bunch of changes:
      • incorporates of course the earlier changes:
        • took some big steps towards portability and Windows compilation, ie using python 2.7 scripts, rather than bash scripts, and fixing many Windows-related compilation issues
        • the plugin architecture
        • factorizing the partial NVIDIA® CUDA™ cuDNN API implementation into a new plugin coriander-dnn
      • moved the default installation directory from /usr/local to ~/coriander
        • this means that plugins can be installed without sudo
        • it also makes it relatively easy to wipe and reinstall, for more effective jenkins testing
      • install_distro.py is now considerably more tested than a few days ago, and handles downloading llvm-4.0 automatically
  • Older news