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 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

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

Libraries

Coriander uses the following libraries:

• clang/llvm: c/c++ parser/compiler; many contributors
• CLBlast: Cedric Nugteren's excellent BLAS for OpenCL
• 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

• Eigen-CL: Minimally-tweaked fork of Eigen, for OpenCL 1.2
• tf-coriander: Tensorflow for OpenCL-1.2

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 4:
  • added cmake macros cocl_add_executable and cocl_add_library
  • these replace the previous add_cocl_executable, and have the advantage that they are standard targets, that you can use with target_link_libraries and so on
  • see cmake usage
• May 31:
  • added a developer debugging option COCL_DUMP_CONFIG, to allow easy inspection of buffers returned by kernel calls, see options
• Older news