mikeymikec, sequential reads are up to 5 times faster than random reads.
I suggest you read my post further up, to understand what the difference is between a fragmented file system, and a defragmented file system.
At this point, the drive geometry matters much less, than for HDDs, but the FS virtual geometry still matters, as long as we linearly address the SSD (never mind how it translates those addresses - this is almost irrelevant, except when writing a FS defragmenter specifically for SSDs, where this internal address translation can be exlpoited to defrag with O(n) (n = number of files) writes to flash outside the addressing table.)
I think that's the point Fred B was trying to make. SSD fragmentation indeed doesn't matter. FS fragmentation (the mapping of files to block ids) still matters, in some applications, as evidenced by benchmarks showing slower reads.
If you use a single FS for your SSD, and store the logs for multiple processes running on a server/cluster on it, and then want to read those logs at high throughput, to do some automatic performance/quality analysis, then you want to make sure that these log files do not fragment. Arguably, you do not want to defragment them either, since the performance impact would kill the application, but you have to take steps in advance to prevent fragmentation, either by using separate file systems or by creating empty files to make sure your log files are placed at a sufficient offset.
How it looks like on the SSD matters very little, but read-ahead increases performance massively, and there are fragmentation-sensitive workloads, so the problem should still be treated seriously.
This is EXACTLY liek the discussion of RAM speed and latency. IF your workload works well with cache, and doesn't do much streaming, memory speed and latency aren't relevant. If you have streaming applications (think GPU-like operations using SSE or AVX) on large datasets, then you need that bandwidth. Memory latency is mostly useful if you have a large dataset, and you cannot just stream the data through, but need to access specific points as a result of a calculation. That's an even rarer use case (large scale AI would come to mind), but still, there ARE use cases, for high speed, low latency main memory.
And there ARE use cases for managing fragmentation of filesystems that mostly reside on SSDs.