#Nvidia a100 benchmarks software

Once a set of options for the intended GEMM operation is identified by the user, these options can be used repeatedly for different inputs. It provides flexibility through parameter programmability for the following options: The cuBLASLt API is a more flexible solution than cuBLAS specifically designed for GEMM operations in AI and machine learning. This API also provides several extensions like the batched and reduced/mixed-precision versions of the traditional functions. In the case of BLAS 元 GEMMs,, there are more options available for and variables such as host and device references. The cuBLAS API implements the NETLIB BLAS specification in all three levels, with up to four versions per routine: real single, real double, complex single, and complex double precisions, with S, D, C, and Z prefixes, respectively. In general, the higher the API complexity the more suitable the API is for kernel developers.

Table 1 provides an overview of what each API is designed for and where users can expect the best performance. The cuBLASDx API is set to be available in Early Access in 2023 and targets GEMMs and their fusion inside device functions. However, the cuBLAS library also offers cuBLASXt API targeting single-node multiGPU GEMMs. This post mainly discusses the new capabilities of the cuBLAS and cuBLASLt APIs. The cuBLAS library is an implementation of Basic Linear Algebra Subprograms (BLAS) on top of the NVIDIA CUDA runtime, and is designed to leverage NVIDIA GPUs for various matrix multiplication operations. This post explores the latest capabilities of the NVIDIA cuBLAS library in CUDA 12.0 with a focus on the recently introduced FP8 format, GEMM performance on NVIDIA Hopper GPUs, and improvements to the user experience such as the new 64-bit integer application programming interface (API) and new fusions.īefore diving into these capabilities, a brief summary details the currently available cuBLAS APIs, how each can be more effectively applied, and how cuBLAS relates to other available NVIDIA tools for matrix multiplications. These components include fusions with bias and popular activation functions and their derivatives.

#Nvidia a100 benchmarks software

The prominence of GEMMs makes it critical for deep learning software to maximally leverage hardware used for matrix multiplications and, at the same time, support several key AI components. GEMMs are also present in forward and backward passes of deep learning training, as well as in inference. The NVIDIA H100 Tensor Core GPU, based on the NVIDIA Hopper architecture with the fourth generation of NVIDIA Tensor Cores, recently debuted delivering unprecedented performance and sweeping AI benchmarks such as MLPerf training.Ī significant fraction of operations in AI and machine learning benchmarks are general matrix multiplications (GEMMS), which are also referred to as matmul functions.