So let’s say you have this massive dataset that needs to be processed by your fancy computer, and it’s stored in some kind of external storage device (like a hard drive or SSD). To access this data, your computer has to send out requests for specific pieces of information, which can take time depending on how far away they are from the memory.

Now imagine that instead of having all this data spread out across different devices and locations, you have it stored in one big chunk called unified memory. This means that everything is right there at your fingertips (or rather, your computer’s fingers), which can significantly reduce the time it takes to access the information you need.

But here’s where things get interesting: when we talk about sequential access analysis for this type of memory on an NVIDIA DGX-2, what we really mean is that we’re looking at how data is accessed in a specific order (i.e., one piece after the other) and analyzing the performance implications of doing so.

For example, let’s say you have this massive dataset stored in unified memory on your fancy computer, and you want to process it using some kind of algorithm that requires sequential access. This means that instead of randomly jumping around from one piece of data to another (which can be slow and inefficient), you’re going through the information in a specific order (i.e., one after the other).

Now, depending on how your computer is set up and what kind of algorithm you’re using, this sequential access might have different performance implications. For example, if you’re working with large datasets that require a lot of memory bandwidth, then having everything stored in unified memory can significantly reduce the time it takes to process the information (since there are no delays caused by sending requests back and forth between devices).

But on the other hand, if your computer is set up in such a way that it’s optimized for parallel processing (i.e., working with multiple pieces of data at once), then having everything stored in unified memory might not be as efficient since you can’t take advantage of the full potential of your hardware.

So when we talk about sequential access analysis on an NVIDIA DGX-2, what we really mean is that we’re looking at how data is accessed in a specific order and analyzing the performance implications of doing so for different types of algorithms and workloads. By understanding these performance characteristics, we can optimize our systems to get the best possible results (i.e., faster processing times and better overall efficiency).