Table of Contents
What is the code for CUDA cublas curand thrust?
The code for this tutorial is on GitHub: https://github.com/sol-prog/cuda_cublas_curand_thrust. Matrix multiplication is an essential building block for numerous numerical algorithms, for this reason most numerical libraries implements matrix multiplication.
How to do matrix multiplication on GPU using CUDA?
Our first example will follow the above suggested algorithm, in a second example we are going to significantly simplify the low level memory manipulation required by CUDA using Thrust which aims to be a replacement for the C++ STL on GPU. Let’s start by allocating space for our three arrays on CPU and GPU:
Can you multiply two arrays on CUDA with cublas?
While the reference BLAS implementation is not particularly fast there are a number of third party optimized BLAS implementations like MKL from Intel, ACML from AMD or CUBLAS from NVIDIA. In this post I’m going to show you how you can multiply two arrays on a CUDA device with CUBLAS.
What is the performance of CUDA math libraries?
Performance may vary based on OS version and motherboard configuration • cuFFT 6.5 on K40c, ECC ON, 32M elements, input and output data on device •Excludes time to create cuFFT “plans” 0 100 200 300 400 500 600 700 800 1 3 5 7 9 11 13 15 17 19 21 23 25 27 S log2(transform_size) Single Precision
The code for this tutorial is on GitHub: https://github.com/sol-prog/cuda_cublas_curand_thrust. Matrix multiplication is an essential building block for numerous numerical algorithms, for this reason most numerical libraries implements matrix multiplication.
What do you need to know about the cublas API?
The cuBLASXt API (starting with CUDA 6.0), and The cuBLASLt API (starting with CUDA 10.1) To use the cuBLAS API, the application must allocate the required matrices and vectors in the GPU memory space, fill them with data, call the sequence of desired cuBLAS functions, and then upload the results from the GPU memory space back to the host.
While the reference BLAS implementation is not particularly fast there are a number of third party optimized BLAS implementations like MKL from Intel, ACML from AMD or CUBLAS from NVIDIA. In this post I’m going to show you how you can multiply two arrays on a CUDA device with CUBLAS.
Why is the cublas library not initialized in CUDA?
The cuBLAS library was not initialized. This is usually caused by the lack of a prior cublasCreate() call, an error in the CUDA Runtime API called by the cuBLAS routine, or an error in the hardware setup.