Chill out, don’t worry, because we’re going to break it down in the most casual way possible.

First off, let’s start with what exactly is a “strided” access? Well, bro, it’s when you want to read or write data from memory that isn’t necessarily next to each other.

Now, why would we even want to do this on a GPU? Well, because sometimes life isn’t fair and not all data is neatly organized in contiguous memory blocks. And let’s be real here, who has time for that kind of organization anyway?

So, how does NVIDIA Tesla V100 handle strided accesses? Let me tell you, it’s like a magician pulling rabbits out of hats except instead of rabbits, we get data. The GPU uses something called “shared memory” to help with these strides. Shared memory is basically a small amount of memory that can be accessed by all the cores on the GPU at once. It’s like having your own personal pantry for each core!

But here’s where it gets interesting shared memory isn’t free, my friend. There’s something called “effective bandwidth” that comes into play when we’re talking about strided accesses on NVIDIA Tesla V100 GPUs. Effective bandwidth is like the speed limit on a highway you can have all the lanes in the world, but if there are too many cars trying to get through at once, it’s going to slow down.

So how do we optimize for effective bandwidth when dealing with strided accesses? Well, bro, that’s where our coding skills come into play. We can use techniques like “tiling” and “coalescing” to help improve the performance of these operations. Tiling is like breaking up a large task into smaller ones it helps reduce the number of memory transactions needed for each operation. Coalescing, on the other hand, is like grouping similar tasks together so that they can be executed more efficiently.

But let’s not forget about the elephant in the room here performance! How much faster are strided accesses with NVIDIA Tesla V100 compared to traditional memory access? Well, bro, it depends on a variety of factors like data size and pattern, but studies have shown that using shared memory can result in up to 2x improvement in effective bandwidth.

It’s not exactly a walk in the park, bro, but it’s definitely worth exploring if you want to get the most out of your GPU. And who knows? Maybe one day we’ll all have our own personal pantry for every core!