A post introduces how tensors are arranged in GPU memory and explains strides, offsets, and `tl.make_block_ptr`, using visual resources to help reason about data access in Triton kernels.
The material discusses how tensors are arranged in memory and introduces `tl.make_block_ptr`, a Triton feature for working with data blocks in GPU kernels.
As you study the topic, start by examining the visual representation of memory. Relate the strides and offsets described in the post to how data is accessed.
Then use that analysis to reason about data access in the kernel. The material may help engineers understand these concepts, but it does not claim that they alone solve performance problems.
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