On Tue, Dec 30, 2014 at 8:14 PM, Yiannis Papadopoulos
Hi,
This is my review of Boost.Compute:
1. What is your evaluation of the design?
It seems logical to me. It is effectively a wrapper around OpenCL that provides implementations of higher-level algorithms, and allows interoperability with OpenCL and OpenGL.
The Boost.Compute name is a bit misleading, as Boost.Compute supports only OpenCL-enabled devices.
2. What is your evaluation of the implementation?
There is some code duplication (e.g. type traits) and various other bits and pieces that can be moved to existing Boost components. I think there should be some effort spent towards that.
Could you let me know which type-traits you think are duplicated or should be moved elsewhere?
It seems that performance is on par with Thrust. However, there are other libraries out there (e.g Bolt) and multiple devices, so there has to be a more extensive experimental evaluation to say decidedly that it is a good implementation.
There are a large number of performance benchmarks under the "perf" directory [1] which can be used to measure and evaluate performance of the library. But you're right that the performance page in the documentation currently only shows comparisons with the STL and Thrust, I'll work on adding others to this.
3. What is your evaluation of the documentation?
Overall, it is pretty good. Given the complexity of the accelerator programming model, a few more elaborate examples in the tutorial would be welcome.
Fully agree, I will continue to work on improving the documentation.
4. What is your evaluation of the potential usefulness of the library?
This is difficult to answer. A lot of work has been put in this library and it seems the way to go. The interfaces are clean, the code looks solid and the developer willing.
However, there is limited vendor support, there are not enough benchmarks and there are other alternatives that they have both. Given that Boost.Compute is targeted to users that know a thing or two about performance, I don't know how they can be convinced to consider using Boost.Compute against Bolt or Thrust.
5. Did you try to use the library? With what compiler? Did you have any problems?
I did using an AMD 7850 on Linux with gcc 4.8. The few examples I tried, compiled and ran fine.
6. How much effort did you put into your evaluation? A glance? A quick reading? In-depth study?
I went over the documentation, I glanced over the code and ran a few examples.
7. Are you knowledgeable about the problem domain?
I'm in the HPC field. I have extensive experience with MPI, OpenMP, pthreads, and less with TBB, CUDA and OpenCL.
8. Do you think the library should be accepted as a Boost library?
This will be a maybe. It is a well-written library with a few minor issues that can be resolved.
However, why would someone use Boost.Compute against what is out there? Average users can resort to Bolt or Thrust. Power users will probably always try to hand-tune their OpenCL or CUDA algorithm. How can we test it and prove its performance?
Yes, Thrust and Bolt are alternatives. The problem is that each is incompatible with the other. Thrust works on NVIDIA GPUs while Bolt only works on AMD GPUs. Choosing one will preclude your code from working on devices from the other. On the other hand, code written with Boost.Compute will work on any device with an OpenCL implementation. This includes NVIDIA GPUs, AMD GPUs/CPUs, Intel GPUs/CPUs as well as other more exotic architectures (Xeon Phi, FPGAs, Parallella Epiphany, etc.). Furthermore, unlike CUDA/Thrust, Boost.Compute requires no special complier or compiler-extensions in order to execute code on GPUs, it is a pure library-level solution which is compatible with any standard C++ compiler. Also, Boost.Compute does allow for users to access the low-level APIs and execute their own hand-rolled kernels (and even interleave their custom operations with the high-level algorithms available in Boost.Compute). I think using Boost.Compute in this way allows for both rapid development and the ability to fully-optimize kernels for specific operations where necessary. Thanks for the review. Let me know if I can explain anything more clearly. -kyle [1] https://github.com/kylelutz/compute/tree/master/perf