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| -rw-r--r-- | README.md | 13 |
1 files changed, 11 insertions, 2 deletions
@@ -9,7 +9,7 @@ CLBlast: The tuned OpenCL BLAS library CLBlast is a modern, lightweight, performant and tunable OpenCL BLAS library written in C++11. It is designed to leverage the full performance potential of a wide variety of OpenCL devices from different vendors, including desktop and laptop GPUs, embedded GPUs, and other accelerators. CLBlast implements BLAS routines: basic linear algebra subprograms operating on vectors and matrices. See [the CLBlast website](https://cnugteren.github.io/clblast) for performance reports on various devices as well as the latest CLBlast news. -This preview-version is not yet tuned for all OpenCL devices: __out-of-the-box performance on some devices might be poor__. See below for a list of already tuned devices and instructions on how to tune yourself and contribute to future releases of the CLBlast library. +This preview-version is not yet tuned for all OpenCL devices: __if out-of-the-box performance is poor, please run the tuners first__. See below for a list of already tuned devices and instructions on how to tune yourself and contribute to future releases of the CLBlast library. Why CLBlast and not clBLAS or cuBLAS? @@ -309,7 +309,7 @@ Since there is no half-precision data-type in C or C++, OpenCL provides the `cl_ * `half FloatToHalf(const float value)`: Converts a 32-bits floating-point value to a 16-bits floating-point value. * `float HalfToFloat(const half value)`: Converts a 16-bits floating-point value to a 32-bits floating-point value. -The `samples/haxpy.c` example shows how to use these convencience functions when calling the half-precision BLAS routine HAXPY. +The `samples/haxpy.c` example shows how to use these convenience functions when calling the half-precision BLAS routine HAXPY. Contributing @@ -338,6 +338,15 @@ Tuning and testing on a variety of OpenCL devices was made possible by: * [ArrayFire](http://arrayfire.org) +More information +------------- + +Further information on CLBlast is available through the following links: + +* A 20-minute presentation of CLBlast was given at the GPU Technology Conference in May 2017. A recording is available on the [GTC on-demand website](http://on-demand.gputechconf.com/gtc/2017/video/s7280-nugteren-clblast.mp4) (poor audio quality however) and a full slideset is also available [as PDF](http://on-demand.gputechconf.com/gtc/2017/presentation/s7280-cedric-nugteren-clblast.pdf). +* More in-depth information and experimental results are also available in a scientific paper titled [CLBlast: A Tuned OpenCL BLAS Library](https://arxiv.org/abs/1705.05249) (May 2017). For CLTune, see also the [CLTune: A Generic Auto-Tuner for OpenCL Kernels](https://arxiv.org/abs/1703.06503) paper. + + Support us ------------- |