About Nikolay Markovskiy

Nikolay Markovskiy
Nikolay is HPC DevTech engineer at NVIDIA. He has PhD in Physical Chemistry from University of Southern California. He has experience in scientific research and software development focusing on computational techniques related to physics, chemistry, and biology.

Drop-in Acceleration of GNU Octave

cuBLAS is an implementation of the BLAS library that leverages the teraflops of performance provided by NVIDIA GPUs.  However, cuBLAS can not be used as a direct BLAS replacement for applications originally intended to run on the CPU. In order to use the cuBLAS API:

  • a CUDA context first needs to be created
  • a cuBLAS handle needs to be initialized
  • all relevant data needs to be copied to preallocated GPU memory, followed by deallocation after the computation

Such an API permits the fine tuning required to minimize redundant data copies to and from the GPU in arbitrarily complicated scenarios such that maximum performance is achieved.  But it is less convenient when just a few BLAS routines need to be accelerated (simple data copy) or when vast amounts of code need to be modified (large programmer effort).  In these cases it would be useful to have an API which managed the data transfer to and from the GPU automatically and could be used as a direct replacement for CPU BLAS libraries.

Additionally, there is the common case where the input matrices to the BLAS operations are too large to fit on the GPU.  While using the cuBLAS API to write a tiled BLAS implementation (which achieves even higher performance) is straightforward, a GPU BLAS library which implemented and managed such tiling in a near optimal way would certainly facilitate access to the computing power of the GPU.

To address these issues, CUDA 6 adds new Multi-GPU extensions, implemented for the most compute intensive BLAS Level 3 routines. They are called cuBLAS-XT and can work directly with host data, removing the need to manually allocate and copy data to the GPU’s memory. NVBLAS is a dynamic library built on top of these extensions which offers a transparent BLAS Level 3 acceleration with zero coding effort.  That is, CPU BLAS libraries can be directly replaced with NVBLAS.  As such, NVBLAS can be used to easily accelerate any application which uses level-3 BLAS routines.
Continue reading