## What is batched GEMM?

The “Batch GEMM” interface resembles the GEMM interface. It is simply a matter of passing arguments as arrays of pointers to matrices and parameters, instead of as matrices and the parameters themselves. Each group consists of multiplications of the same matrices shape (same m, n, and k) and the same parameters.

### What is cuBLAS library?

The cuBLAS Library provides a GPU-accelerated implementation of the basic linear algebra subroutines (BLAS). cuBLAS accelerates AI and HPC applications with drop-in industry standard BLAS APIs highly optimized for NVIDIA GPUs. The cuBLAS library is included in both the NVIDIA HPC SDK and the CUDA Toolkit.

#### How do you test a cuBLAS?

You could copy an example of C code that uses cuBLAS from https://docs.nvidia.com/cuda/cublas/index.html and then try to compile it: nvcc cublas_test. c -o cublas_test. out -lcublas and then run it: ./cublas_test. out .

**Does cuBLAS use tensor core?**

cuBLAS uses Tensor Cores to speed up GEMM computations (GEMM is the BLAS term for a matrix-matrix multiplication); cuDNN uses Tensor Cores to speed up both convolutions and recurrent neural networks (RNNs). Many computational applications use GEMMs: signal processing, fluid dynamics, and many, many others.

**Which is the best batched matrix multiply in cublas?**

Batched and strided batched matrix multiply (GEMM) functions are now available in cuBLAS 8.0 and perform best on the latest NVIDIA Tesla P100 GPUs. You can find documentation on the batched GEMM methods in the cuBLAS Documentation to get started at peak performance right away!

## Which is an example of a batched GEMM?

Besides the batched GEMM in cuBLAS, there have been a number of research papers on batched GEMM, developed as needed for particular applications. For example, a batched GEMM for very small sizes (up to 16) was developed for a high-order \\fnite element method (FEM) [12].

### Is there a batched GEMM kernel for GPUs?

This paper presents a high performance batched GEMM kernel on Graph- ics Processing Units (GPUs). We address batched problems with both xed and variable sizes, and show that specialized GEMM designs and a comprehensive autotuning process are needed to handle problems of small sizes.

#### How to destroy the context of a cublas function?

The cublasHandle_t type is a pointer type to an opaque structure holding the cuBLAS library context. The cuBLAS library context must be initialized using cublasCreate() and the returned handle must be passed to all subsequent library function calls. The context should be destroyed at the end using cublasDestroy().