![]() For Fortran code ported to C in mechanical fashion, one may chose to retain 1-based indexing to avoid the need to transform loops. ![]() Instead, macros or inline functions should be defined to implement matrices on top of one-dimensional arrays. Since C and C++ use row-major storage, applications written in these languages can not use the native array semantics for two-dimensional arrays. Data Layout įor maximum compatibility with existing Fortran environments, the cuBLAS library uses column-major storage, and 1-based indexing. This is analogous to how cuFFT and FFTW first create a plan and reuse for same size and type FFTs with different input data. After a set of options for the intended GEMM operation are identified by the user, these options can be used repeatedly for different inputs. This library adds flexibility in matrix data layouts, input types, compute types, and also in choosing the algorithmic implementations and heuristics through parameter programmability. The cuBLASLt is a lightweight library dedicated to GEneral Matrix-to-matrix Multiply (GEMM) operations with a new flexible API. To use the cuBLASXt API, the application may have the data on the Host or any of the devices involved in the computation, and the Library will take care of dispatching the operation to, and transferring the data to, one or multiple GPUs present in the system, depending on the user request. The cuBLAS API also provides helper functions for writing and retrieving data from the GPU. 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. The cuBLASLt API (starting with CUDA 10.1) ![]() ![]() The cuBLASXt API (starting with CUDA 6.0), and The cuBLAS API, which is simply called cuBLAS API in this document (starting with CUDA 6.0), The cuBLAS Library exposes three sets of API: It allows the user to access the computational resources of NVIDIA Graphics Processing Unit (GPU). The cuBLAS library is an implementation of BLAS (Basic Linear Algebra Subprograms) on top of the NVIDIA®CUDA™ runtime. The API Reference guide for cuBLAS, the CUDA Basic Linear Algebra Subroutine library. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |