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Updated README and added issue templates. (#382)

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Andrew Kerr
2021-12-17 09:26:20 -05:00
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@ -56,8 +56,11 @@ CUTLASS 2.8 is an update to CUTLASS adding:
CUTLASS primitives are very efficient. When used to construct device-wide GEMM kernels,
they exhibit performance comparable to cuBLAS for scalar GEMM
computations. The above figure shows CUTLASS performance relative to cuBLAS
for large matrix dimensions on an NVIDIA GeForce 2080 Ti, an NVIDIA A100, and an NVIDIA TitanV
using CUDA 11.0 Toolkit. Tensor Core operations are implemented using CUDA's
for large matrix dimensions on an [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/),
an [NVIDIA A2](https://www.nvidia.com/en-us/data-center/products/a2/),
an [NVIDIA TitanV](https://www.nvidia.com/en-us/titan/titan-v/),
and an [NVIDIA GeForce 2080 Ti](https://www.nvidia.com/en-us/geforce/graphics-cards/rtx-2080-ti/)
compiled with the [CUDA 11.5 Toolkit](https://developer.nvidia.com/cuda-downloads). Tensor Core operations are implemented using CUDA's
[mma instruction](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-mma).
# Compatibility