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#Wmma 5 picture pack windows#

Added Cooperative Groups(CG) support to several samples notable ones to name are 6_Advanced/cdpQuadtree, 6_Advanced/cdpAdvancedQuicksort, 6_Advanced/threadFenceReduction, 3_Imaging/dxtc, 4_Finance/MonteCarloMultiGPU, 0_Simple/matrixMul_nvrtc.Demonstrates a conjugate gradient solver on GPU using Multi Block Cooperative Groups. Added 6_Advanced/conjugateGradientMultiBlockCG.Demonstrates single pass reduction using Multi Block Cooperative Groups. Added 6_Advanced/reductionMultiBlockCG.Demonstrates warp aggregated atomics using Cooperative Groups. Added 6_Advanced/warpAggregatedAtomicsCG.Demonstrates Spectral Clustering using NVGRAPH Library. Added 7_CUDALibraries/nvgraph_SpectralClustering.

#Wmma 5 picture pack windows#

Added windows support to 6_Advanced/c++11_cuda.

Added two new reduction kernels in 6_Advanced/reduction one which demonstrates reduce_add_sync intrinstic supported on compute capability 8.0 and another which uses cooperative_groups::reduceįunction which does thread_block_tile level reduction introduced from CUDA 11.0.

Removed 7_CUDALibraries/nvgraph_Pagerank, 7_CUDALibraries/nvgraph_SemiRingSpMV, 7_CUDALibraries/nvgraph_SpectralClustering, 7_CUDALibraries/nvgraph_SSSP as the NVGRAPH library is dropped from CUDA Toolkit 11.0.

Demonstrates compressible memory allocation using cuMemMap API.

Added 6_Advanced/cudaCompressibleMemory.

Demonstrates binary_partition cooperative groups creation and usage in divergent path.

Added warp aggregated atomic multi bucket increments kernel using labeled_partition cooperative groups in 6_Advanced/warpAggregatedAtomicsCG which can be used on compute capability 7.0 and above GPU architectures.

Also makes use of asynchronousĬopy from global to shared memory using cuda pipeline which leads to further performance gain.

Demonstrates tf32 (e8m10) GEMM computation using the WMMA API for tf32 employing the Tensor Cores. Demonstrates _nv_bfloat16 (e8m7) GEMM computation using the WMMA API for _nv_bfloat16 employing the Tensor Cores. Makes use of asynchronous copy from global to shared memory using cuda pipeline which leads to further performance gain. Demonstrates double precision GEMM computation using the WMMA API for double precision employing the Tensor Cores. Demonstrates the stream attributes that affect L2 locality. Demonstrates asynchronous copy of data from global to shared memory using cuda pipeline. Added 0_Simple/globalToShmemAsyncCopy.