![]() In addition, the new CUDA Toolkit 3.2 release includes H.264 encode/decode, new Tesla Compute Cluster (TCC) integration, cluster management features, and support for the new 6GB NVIDIA Tesla and Quadro GPU products. A host of additional improvements to GPU debugging and performance analysis tools.New CUSPARSE library of sparse matrix routines.Up to 300% performance improvement in CUDA BLAS (CUBLAS) library routines.The CUDA Toolkit includes all the tools, libraries and documentation developers need to build CUDA C/C++ applications, and is the foundation for many other GPU computing language solutions.Īccording to the company, new features and significant performance enhancements in version 3.2 include: Please refer to additional information and installation instructions in the README file distributed with the patch.NVIDIA has announced the availability of the CUDA Toolkit 3.2 production release, which provides performance increases, new math libraries and advanced cluster management features for developers creating GPU-accelerated applications. The patch is available as "CUDA Toolkit: GFEC Patch for MacOS" from the following location: To address this issue, NVIDIA has released a patch that updates components of nvcc. The version of nvcc included with CUDA Toolkit 3.2 fails to handle variables of type size_t as an 8-byte entity in PTX when compiling 64-bit device code. * On MacOS only, the NVIDIA C Compiler (nvcc) handles size_t incorrectly during 64-bit compilation. Un-check the "Automatic graphics switching" check box in the upper left ![]() In order to ensure that your CUDA-capable GPU is not powered down by the operating system do the following:ģ. If the operating system has powered down the CUDA-capable GPU, CUDA fails to run and the system returns an error that no device was found. * To save power, some Apple products automatically power-down the CUDA-capable GPU in the system. This issue will be fixed in the next release of the CUDA Toolkit. A workaround for this issue is to round the size of the memory allocated for matrix "A" up to the next highest multiple of 64 bytes. Under certain conditions, this can lead to a kernel launch failure (though in no circumstances does it lead to incorrect results). * For the CGEMM kernel used in some instances on Fermi GPUs when "m" is not a multiple of 16, a few bytes past the end of the "A" matrix are unnecessarily fetched. Given threshold size T, where T is equal to 2^27 - 512 (i.e., 134217216), the crash might be seen in any of the following circumstances:ġ) A is not transposed, lda * k >= T, and T is divisible by lda.Ģ) B is not transposed, ldb * n >= T, T is divisible by n, and n is divisible by 32ģ) A is transposed, lda * m >= T, T is divisible by m, and m is divisible by 32Ĥ) B is transposed, ldb * k >= T, and T is divisible by ldb. To work around this problem, the input to CUBLAS must be recursively subdivided until the individual calls to these CUBLAS routines do not match these criteria. * In CUBLAS 3.2, the GEMM, SYRK, and HERK routines for Fermi GPUs can enter an infinite recursion leading to an application crash for certain input sizes meeting the criteria below. counter names should be "inst_*" and not "instructions_*". The correct counter names for compute capability 2.0 or higher are as follows: ![]() Get the latest from the 3.2 downloads page: * The command line Compute Profiler document "Compute_Profiler.txt" in your installation directory has incorrect counter names for "instructions_*". * The document- ptx_isa_2.2.pdf in your installation directory has been updated. NVIDIA CUDA Toolkit v3.2 Errata for Windows, Linux, and MacOS X ![]()
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