CLBlast: Tuning for better performance

This document describes how to tune CLBlast for better performance and lists for which devices tuned kernels are already available. For other information about CLBlast, see the main README.

Already tuned-for devices

The CLBlast library is already tuned for the most commonly used OpenCL devices and it's gradually being extended to other devices as well. For unseen devices CLBlast will make use of common-best tuning values for similar architectures (e.g. AMD Fiji) or in general similar devices (e.g. AMD GPUs), so performance might still be decent. The current release of CLBlast is tuned for the following devices:

NVIDIA GPUs:
- GRID K520
- GeForce GT 650M
- GeForce GTX 480
- GeForce GTX 580
- GeForce GTX 670
- GeForce GTX 680
- GeForce GTX 750
- GeForce GTX 750 Ti
- GeForce GTX 760 Ti
- GeForce GTX 920MX
- GeForce GTX 970
- GeForce GTX 980
- GeForce GTX 1070
- GeForce GTX 1080
- GeForce GTX 1080 Ti
- GeForce GTX TITAN
- GeForce GTX TITAN Black
- GeForce GTX TITAN X
- TITAN X (Pascal)
- Tesla K20m
- Tesla K40m
AMD GPUs:
- Radeon HD 6750M
- Radeon HD 6770M
- Radeon HD 7970
- Radeon R9 270X
- Radeon R9 290X
- Radeon R9 M370X
- Radeon R9 380
- Radeon RX 480
- Radeon R9 Fury X
- Radeon Pro 580
Intel GPUs:
- HD Graphics 530
- HD Graphics 5500 BroadWell U-Processor GT2
- HD Graphics 630
- HD Graphics Haswell Ultrabook GT2 Mobile
- HD Graphics IvyBridge M GT2
- HD Graphics Skylake ULT GT2
- Iris
- Iris Pro
Intel CPUs:
- Core i5-4570
- Core i5-6200U
- Core i7-920
- Core i7-2670QM
- Core i7-3770K
- Core i7-4790K
- Core i7-5930K
- Core i7-6770HQ
Other devices:
- ARM Mali-T628 GPU
- ARM Mali-T760 GPU
- Qualcomm Adreno 330 GPU
- Intel MIC

If your device is not (yet) among this list or if you want to tune CLBlast for specific parameters (e.g. rectangular matrix sizes), you should run the included tuners.

Compiling and running the tuners

The included CLBlast tuners are compiled with the default CMake options. If they are not compiled, make sure you are specifing -DTUNERS=ON, for example as follows:

cmake -DTUNERS=ON ..

Compiling with -DTUNERS=ON will generate a number of tuners, each named clblast_tuner_xxxxx, in which xxxxx corresponds to a .opencl kernel file as found in src/kernels. These kernels corresponds to routines (e.g. xgemm) or to common pre-processing or post-processing kernels (copy and transpose). Running such a tuner will test a number of parameter-value combinations on your device and report which one gave the best performance. Running make alltuners runs all tuners for all precisions in one go. You can set the default device and platform for alltuners by setting the CLBLAST_DEVICE and CLBLAST_PLATFORM environmental variables. Alternatively, you can also manually run each of the tuners for each of the precisions. Here's an example to tune the axpy kernels for 64-bit precision on device 0 of platform 0:

./clblast_tuner_xaxpy --precision 64 --device 0 --platform 0

The kernels gemm and gemm_direct have too many parameters to explore. Therefore, they will run in two stages: a first stage with a fixed limited number of parameter combinations, and a second stage with a random selection from a much larger search space. The random fraction is determined by the fraction argument on the command-line.

There are also several routine-level tuners. They tune inter-kernel parameters and should only be run after the kernels are tuned. An example is the GEMM routine tuner, which determines when to use the direct or the in-direct GEMM kernel.

Using the tuning results

The tuners output a JSON-file with the results. The best results need to be added to src/database/kernels/xxxxx.hpp in the appropriate section. However, this can be done automatically based on the JSON-data using a Python (2.7 or 3.x) script in scripts/database/database.py. If you want the found parameters to be included in future releases of CLBlast, please attach the JSON files to the corresponding issue on GitHub or email the main author.

In summary, tuning the entire library for your device can be done as follows (starting from the root of the CLBlast folder):

mkdir build
cd build
cmake -DTUNERS=ON ..
make
make alltuners
python ../scripts/database/database.py . ..
make

After the kernels are tuned, you can run the clblast_tuner_routine_xgemm tuner to optimize the high-level GEMM routine, i.e. selecting which method to use: the direct kernel or the in-direct kernel.

Tuning using the API (advanced users only)

Apart from running the tuning binaries, it is also possible to run the tuners programmatically through the CLBlast API. This could be useful if you want to tune for non-standard arguments (e.g. a rectangular or very small matrix). The tuning results can then also be set programmatically using OverrideParameters.

The tuning API does not perform any disk or stdout I/O, thus it is not possible to track progress. Running the regular tuner binaries should give an idea of the amount of configurations to explore for a particular device, thus giving an indication of a good value for the fraction argument (see the API documentation for more details).

Inspecting and changing tuning parameters at run-time

Alternatively, you can also supply your tuning parameters programmatically through the CLBlast API. This is especially useful if you tune for specific non-standard arguments (e.g. a rectangular or a very small matrix). To do so, you can call the OverrideParameters function which will set new parameters for a specific kernel. At the first next call of the target routine, CLBlast will compile a new binary and use it together with the new parameters from then on. Until OverrideParameters is called again of course. This is the API:

StatusCode PUBLIC_API OverrideParameters(const cl_device_id device, const std::string &kernel_name,
                                         const Precision precision,
                                         const std::unordered_map<std::string,size_t> &parameters)

To inspect current behaviour, you can also retrieve the parameters for a specific device and kernel combination:

StatusCode PUBLIC_API RetrieveParameters(const cl_device_id device, const std::string &kernel_name,
                                         const Precision precision,
                                         std::unordered_map<std::string,size_t> &parameters)

Tuning OpenCL compiler options

For all of CLBlast's APIs, it is possible to optionally set an OS environmental variable CLBLAST_BUILD_OPTIONS to pass specific build options to the OpenCL compiler. Also make sure this is set in the same way when running the tuners.

Which kernels are used for which routines?

To find out which tuners to run for which routines, you can use the table below. The kernel names correspond to the tuner binaries, the tuner API, and to the arguments for OverrideParameters and RetrieveParameters.

Routines	Kernel(s) / Tuner(s)
AXPY COPY SCAL SWAP OMATCOPY AXPYBATCHED	Xaxpy
AMAX ASUM DOT DOTC DOTU NRM2 SUM MAX MIN AMIN	Xdot
GBMV GEMV HBMV HEMV HPMV SBMV SPMV SYMV TMBV TPMV TRMV TRSV	Xgemv
GER GERC GERU HER HER2 HPR HPR2 SPR SPR2 SYR SYR2	Xger
GEMM HEMM HER2K HERK SYMM SYR2K SYRK TRMM GEMMBATCHED GEMMSTRIDEDBATCHED	Xgemm XgemmDirect Copy Pad Transpose Padtranspose
TRSM	Xgemm XgemmDirect Copy Pad Transpose Padtranspose Invert
IM2COL	Copy

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