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cusparseConstrainedGeMM has been deprecated in favor of cusparseSDDMM.All routines support NVTX annotation for enhancing the profiler time line on complex applications.Better accuracy of cusparseAxpby, cusparseRot, cusparseSpVV for bfloat16 and half regular/complex data types.New routine for Sampled Dense Matrix - Dense Matrix Multiplication (cusparseSDDMM) which deprecated cusparseConstrainedGeMM and provides better performance.New algorithm (CUSPARSE_SPMM_CSR_ALG3) for Sparse Matrix - Matrix Multiplication (cusparseSpMM) with better performance especially for small matrices.Support for deterministic and non-deterministic computation.Support for mixed regular-complex data type computation.Support for regular/complex bfloat16 data types for both uniform and mixed-precision computation.Extended functionalities for cusparseSpMV:.New algorithms for CSR/COO Sparse Matrix - Vector Multiplication (cusparseSpMV) with better performance.New Tensor Core-accelerated Block Sparse Matrix - Matrix Multiplication (cusparseSpMM) and introduction of the Blocked-Ellpack storage format.The user has to link libcusolver.so with the correct version of libcublas.so. However, it breaks backward compatibility. This reduces the binary size of libcusolver.so. libcusolver.so no longer links libcublas_static.a instead, it depends on libcublas.so.GESVDR computes partial spectrum with random sampling, an order of magnitude faster than GESVD. New singular value decomposition (GESVDR) is added.
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This section summarizes the changes in CUDA 11.2.1 (11.2 Update 1) since the 11.2.0 GA release.
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No-copy pinning of system memory, a faster alternative to cudaMallocHost().Use all GPUs in the system concurrently from a single host thread.
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If you would like to be notified of upcoming drivers for Windows, please subscribe here. To workaround, avoid changing the resolution, or disconnecting from or connecting to an external display with the application opened.
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These new drivers will not only deliver the latest features and improvements for NVIDIA RTX GPUs, but also for current and previous generation Quadro GPUs. NVIDIA RTX Enterprise drivers will continue to provide users with the same level of enterprise-class quality, reliability, performance, and security as previous Quadro drivers.
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With the introduction of NVIDIA RTX products powering the next generation of professional visual, compute, and AI platform solutions for the enterprise, the Quadro driver will be transitioning to the NVIDIA RTX Enterprise brand to better align with new and future products.