![]() Their derivatives and asynchronous executions have an unknown/unbounded User object lifetime assistance: Functionality to assist user code in lifetime managementįor user-allocated resources referenced in graphs. ![]() Enhancements to make stream capture more flexible: Functionality to provide read-writeĪccess to the graph and the dependency information of a capturing stream,.Stream ordered memory allocator enhancements.cusparseSnnz produced wrong results for some particular sparsity pattern.ĬusparseScsrsm2_bufferSizeExt have been deprecated.Of rows are greater than 128 * resident CTAs. cusparseCnnz_compress produced wrong results when the number.Improved NTVX trace with distinction between light calls and kernel.All cuSPARSE APIs are now asynchronous on platforms that support stream.Better performance for Blocked-ELL SpMM with block size > 64, doubleĭata type, and alignments smaller than 128-byte on NVIDIA Ampere sm80.Blocke-ELL format now support empty blocks.cusparseDenseToSparse() routine adds the conversionįrom dense matrix (row-major/column-major) to Blocked-ELL format.Introduced a new routine for sparse triangular solver with multiple.Sparse triangular solver adds support for COO format.Supports CSR storage format and mixed-precision computation. Matrix structure is reused for multiple computation. Multiplication ( cusparseSpGEMMreuse) where the output Introduced a new routine for sparse matrix - sparse matrix.To a performance penalty on some problem sizes. Without this, newly added kernels will be excluded and it will likely lead Only returns algo configurations that support both _HOST and _DEVICE modes. To be able to access the fastest possible kernels throughĬublasLtMatmulAlgoGetHeuristic() you need to setĬUBLASLT_MATMUL_PREF_POINTER_MODE_MASK in search preferences toĬUBLASLT_POINTER_MODE_MASK_HOST or CUBLASLT_POINTER_MODE_MASK_NO_FILTERING.īy default, heuristics query assumes the pointer mode may change later and.Linking with static cublas and cublasLt libraries on Linux now requires using gcc-5.2 andĬompatible or higher due to C++11 requirements in these libraries.These epilogues requireĪuxiliary input mentioned in the bullet above. Of the corresponding activation function on matrix C, and produceīias gradient as a separate output. DReLuBGrad and DGeluBGrad epilogues that compute the backpropagation.Which is used on backward propagation to compute the corresponding ReLuBias and GeluBias epilogues that produce an auxiliary output.New epilogues have been added to support fusion in ML training.This limitation is expected to be resolved in a future release. Host pointers are supported for scalars (for example, alpha and beta Some new kernels have been added for improved performance but have the limitation that only.Support for Ubuntu 16.04 is deprecated.Updated the documentation and samples after multi-device cooperative launch.Skipped on Windows (when using the interactive or silent installation) or onįor more information on customizing the install process on Windows, see. Recommended for use in production with Tesla GPUs.įor running CUDA applications in production with Tesla GPUs, it is recommended toĭownload the latest driver for Tesla GPUs from the NVIDIA driver downloads site atĭuring the installation of the CUDA Toolkit, the installation of the NVIDIA driver may be Note that this driver is for development purposes and is not * Using a Minimum Required Version that is different from Toolkitĭriver Version could be allowed in compatibility mode - please read theįor convenience, the NVIDIA driver is installed as part of the CUDA Toolkit CUDA Toolkit and Corresponding Driver Versions CUDA ToolkitĬUDA 10.1 (10.1.105 general release, and updates) Versioned, and the toolkit itself is versioned as shown in the tableĭriver version for CUDA enhanced compatibility is shown below. Note: Starting with CUDA 11.0, the toolkit components are individually More information on compatibility can be found at. The CUDA driver is backward compatible, meaning that applications compiled againstĪ particular version of the CUDA will continue to work on subsequent (later) Įach release of the CUDA Toolkit requires a minimum version of the CUDA driver. Information various GPU products that are CUDA capable, visit. Running a CUDA application requires the system with at least one CUDA capable GPUĪnd a driver that is compatible with the CUDA Toolkit. CUDA 11.3 Component Versions Component Name
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