Gpfs file system basics of investing
Keable says this feature, which is a block-level algorithm and so capable of dealing with ever-larger disk capacities, was released on POWER 7. GPFS 3. Keable says GPFS 3. The filesets can be dynamically moved without taking the filesystem down and the sysadmin can move data across disks' tiers on a per-day or some other time unit basis.
The fileset has an "i-node" associated with it — an i-node being a tag and a block of data — which points to the actual file data and contains metadata such as origination date, time of first access, etc. The GPFS stored all the fileset metadata on one system. With 3. Previously backup policies were applied at the filesystem level, but now, Keable says, "We can apply separate backup policies at the fileset level. It makes the GPFS sysadmin's job easier and more flexible.
So you don't have to do two accesses to get at such small files — for example one for the i-node pointer and then one more for the real data — as the i-node metadata and small file data are co-located. It gets better. A customer's own metadata can be added to the i-node as well. Keable says you could put the latitude and longitude of the file in the i-node and enable location-based activities for such files, such as might be needed in a follow-the-sun scheme.
I knew what our numbers were, but I had to figure out how they compared to these other parallel file systems. Since they all are scale-out parallel file systems, their maximum performance is theoretically limitless, so I needed to find some way to make a fair comparison. The diagram below shows an example of some of the solutions and different form factors on offer.
How to Compare the Performance of These? But how to compare all these systems with different sizes and form factors? I started to compare performance per server and even performance per rack, but our benchmarking engineer, who also had experience benchmarking Lustre and GPFS, recommended I look at throughput per hard disk drive HDD as a good comparable figure of merit , since the number of disks has the primary impact on the system footprint, TCO, and performance efficiency.
IBM advertises read throughput and I will focus on that in this blog since that is the most readily available benchmark number supplied by most vendors , but note that IBM GPFS write speeds can be almost 2 times slower than reads. While being a bit slower, Scatter mode has the valuable customer benefit of maintaining uniform performance as the file system fills, avoiding performance loss from fragmentation, a wildly popular feature not common in most file systems.
PanFS also maintains similarly consistent performance, but uniquely at the much higher performance rates shown. Accumulating newly written data in larger sequential regions reduces data fragmentation, so later reads of the data will also be sequential.
PanFS results are shown below.
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