[Developers] opencl newfmin example
ian.taylor at noaa.gov
Tue May 15 14:59:53 PDT 2012
Thanks to help from Dave, I finally got his example working (perhaps) on a
Windows computer using Microsoft Visual C++ on a computer with a Nvidia
GPU. I got an error about "Error trying to load Kernel source GPU" (pasted
at bottom of email along with other warnings that I don't understand), but
using something called "GPU-Z", I was able to see that the GPU Load went
from 1% to 99%. Nevertheless, using the GPU only cut the run time in half,
and the majority of that was achieved with the BFGS algorithm without the
GPU (USE_GPU_FLAG=0). So I'm thinking the GPU is not being utilized
correctly, or my GPU is not as well suited to this problem as Dave's, or
the VC compiler is not as well suited at GCC.
new newfmin with GPU: 2 minutes, 19 seconds for 442 function evaluations.
new newfmin w/o GPU: 2 minutes, 37 seconds for 682 function evaluations.
old newfmin time (no GPU): 5 minutes, 21 seconds for 2119 function
I had struggles at various points along the way, including installing the
correct OpenCL stuff for my GPU, building ADMB with or without the new
newfmin file, and linking the bigmin model to the OpenCL libraries.
Everything I know about C++, I learned from working with ADMB, so this was
a valuable addition to my education.
### Here are the warnings and errors ###
>bigmin -mno 10000 -crit 1.e-10 -nox -nohess
Error trying to open data input file bigmin.dat
command queue created successfully
Number of devices found 1
Error trying to load Kernel source GPU
All buffers created successfully
Program creation code = 0
Program build code = 0
Create Kernel2 error code = 0
Create Kernel error code = 0
Create Kernel3 error code = 0
Create Kernel4 error code = 0
Create Kernel1 error code = 0
Initial statistics: 6144 variables; iteration 0; function evaluation 0;
On Tue, May 15, 2012 at 10:51 AM, John Sibert <sibert at hawaii.edu> wrote:
> I tried to get it working, but did not succeed. In the process, I might
> have learned a few things, so I have included a lot of stuff in this email.
> It would be really helpful if others on this list would also give it a try
> and share the results with the rest of us.
> The main problem I encountered ignorance of what (if anything) needed to
> be installed on my computer. Neither the OpenCL nor the AMD websites offer
> much guidance.
> In the end I concluded that my hardware (a Dell D series laptop with
> Nvidia graphics processor purchased in 2009 and running Ubuntu 10.04) is
> unsuitable, probably not supporting double precision arithmetic.
> Without installing any new software the machine comes with the executable
> "clinfo" that provides a lot of information about the hardware. Sections to
> note are "Platform Extensions: cl_khr_byte_addressable_store cl_khr_icd
> cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query
> and "Extensions: cl_khr_fp64 cl_amd_fp64 ..." (without the word
> "Platform"). If the graphics card supports double precision calculations it
> should report "cl_khr_fp64 cl_amd_fp64", but note the ambiguity of two
> different "Extensions".
> Emboldened, I managed to build the bigmin example without much drama and
> $ ./bigmin -mno 10000 -crit 1.e-10 -nox -nohess
> produced the following
>> Error creating command queue ret = -34
>> Number of devices found 0
>> No GPU found
> So I desabled the Nvidia graphics driver and downloaded
> AMD-APP-SDK-v2.6-lnx64.tgz from
> and installed it. After messing around with linker paths, the bigmin
> compiled and linked, but produced the same run-time error .
> At his point I concluded that my graphics card does not support floating
> point calculations.
> A bit of work with google turned up some more information.
> lists Nvidia graphics processors and their "compute capability". The entry
> for mine is Quadro NVS 135M compute capability 1.1
> offers some interpretation of compute capacity:
>> To enable the use of doubles inside CUDA kernels you first need to
>> make sure you have a CUDA Compute 1.3-capable card. These are the newer
>> versions of the nVidia CUDA cards such as the GTX 260, GTX 280, Quadro
>> FX 5800, and Tesla S1070 and C1060. Thereby you have to add a command
>> line options to the nvcc compiler: --gpu-architecture sm_13.
> The ever-helpful wikipedia entry for CUDA http://en.wikipedia.org/wiki/*
> *CUDA <http://en.wikipedia.org/wiki/CUDA> agrees
>> CUDA (with compute capability 1.x) uses a recursion-free,
>> function-pointer-free subset of the C language, plus some simple
>> extensions. However, a single process must run spread across multiple
>> disjoint memory spaces, unlike other C language runtime environments.
>> CUDA (with compute capability 2.x) allows a subset of C++ class
>> functionality, for example member functions may not be virtual (this
>> restriction will be removed in some future release). [See CUDA C
>> Programming Guide 3.1 - Appendix D.6]
>> Double precision (CUDA compute capability 1.3 and above) deviate
>> from the IEEE 754 standard: round-to-nearest-even is the only supported
>> rounding mode for reciprocal, division, and square root. In single
>> precision, denormals and signalling NaNs are not supported; only two
>> IEEE rounding modes are supported (chop and round-to-nearest even), and
>> those are specified on a per-instruction basis rather than in a control
>> word; and the precision of division/square root is slightly lower than
>> single precision.
> So you need a graphics processor with compute capability 1.3 and above.
> I would urge everyone to try to get this example running and share your
> experiences. The opencl looks like a promising way to parallelize some
> applications. The overview document
> implies that it might be possible to tune an application to use either GPU
> or multiple cores on a cluster. Unfortunately the learning curve is steep
> (ask Dave) and the documentation is thin.
> Happy hacking,
> John Sibert
> Emeritus Researcher, SOEST
> University of Hawaii at Manoa
> Visit the ADMB project http://admb-project.org/
> On 05/12/2012 05:31 AM, dave fournier wrote:
>> Has anyone else actually got this example to work?
>> Some advice. Older GPU's (whatever that is) probably
>> do not support double precision.
>> WRT using the BFGS update on the CPU. It does not seem
>> to perform as well as doing iton the GPU. I think this is
>> due to roundoff error. The CPU is carrying out additions in a different
>> way. It may be that with say 4K or more parameters and this
>> (artificial) example roundoff error becomes important.
>> I stored the matrix by rows. It is now appears that it should be stored
>> by columns for the fastest matrix * vector multiplication.
>> Developers mailing list
>> Developers at admb-project.org
> Developers mailing list
> Developers at admb-project.org
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