Before anything else: what are GEMMs? Well, they stand for General Matrix Multiplication (Gee-Emm), which is essentially multiplying two matrices together. This might not sound like a big deal, but it’s actually one of the most common operations in deep learning and other AI applications.

Now NVIDIA Tensor Cores these are specialized hardware units that can perform matrix multiplication much faster than traditional CPUs (Central Processing Units). They were designed specifically for AI workloads, which means they’re optimized to handle large amounts of data and complex operations.

So how do we optimize GEMM kernels for NVIDIA Tensor Cores? Well, there are a few different techniques you can use:

1) Use the right algorithm There are several algorithms that can be used for matrix multiplication, but not all of them work well with Tensor Cores. For example, the Gemm kernel (which stands for General Matrix Multiply) is optimized specifically for this hardware and should be your go-to choice if you’re working with large matrices.

2) Use the right data type NVIDIA recommends using 16-bit floating point numbers (half precision) whenever possible, as they can provide significant performance gains without sacrificing accuracy. However, there are some cases where you might need to use full precision (32 bits), so it’s important to test both options and see which one works best for your specific application.

3) Use the right configuration NVIDIA Tensor Cores have several different configurations that can affect performance, including block size and shared memory usage. It’s important to experiment with these settings to find the optimal configuration for your workload.

4) Optimize your code Finally, it’s essential to optimize your code as much as possible to get the best possible performance from NVIDIA Tensor Cores. This might involve using techniques like loop unrolling and register tiling to reduce memory access overhead and improve cache utilization.

Optimizing GEMM kernels for NVIDIA Tensor Cores isn’t exactly rocket science, but it does require some knowledge of AI workloads and hardware optimization. With the right techniques and tools, however, you can achieve significant performance gains that will help your applications run faster and more efficiently than ever before!