HPC bod SGI racks UV brains, reaches 30 MEEELLION IOPS How does 30 million IOPS from a 32-socket server grab you? We've learnt SGI has taken its UV 300H server, used in its "UV for SAP HANA" product, and boosted its performance with 64 Intel DC P3700 SSDs, which use the NVMe interface. The team working on this includes SGI's acquired Starboard Storage engineers. Starboard was …
These Linux servers are nothing more than a cluster, which is evidenced if you look at the workloads they are used for. For instance, SAP HANA is clustered. These SGI clusters can not replace a large SMP server such as 32-socket IBM P795 or Oracle M6-32. Clusters are only used for parallel workloads such as HPC number crunching, never for SMP business workloads.
Just look at all the SGI business cases, in every single case they are used for parallel workloads.
Sorry. You know not from whence you speak. The SGI UV product line scales UP to hundreds of processor sockets and has done so since 2009.
You are correct that there is a clustered version of SAP's HANA platform but they also support an "in-memory" scale-up capability. At a recent SAP sponsored event, there was a demonstration showing complex queries on a fully in-memory wikipedia based data set of several tens of terabytes completing in a second or less. Yes SGI also markets high end and mid-range clusters. UV300 is not one of them.
@waterman
You are misinformed. There are (at least) two different types of scaling:
1) horizontal/scale-out scaling, i.e. a cluster (typically have 10.000s of cores and 100s of TB RAM)
2) vertical/scale-up scaling, i.e. a single large fat server (typically have 16-32 sockets and 8 TB RAM) in a SMP style
The SGI servers have always been clusters. Even SGI confirms this. Whenever you see a server larger than 16/32 sockets, you know you are dealing with a cluster. Clusters are only used for cluster workloads, such as HPC number crunching workloads or other parallel workloads. Clusters are typically serving a single user, a scientist that starts up a workload running for hours/days. All the large Linux servers having 10.000s of cores out there, are clusters. Clusters are very very cheap, you basically pay the hardware cost. If you buy a cluster with 100 cpus, it costs 100 x 1 cpu.
SMP servers are very very expensive and difficult to make. In this category you have large Unix servers and Mainframes. IBM 32 socket Unix P795 server for the old TPC-C world record, costed $35 million. Yes, one single server. To create a huge server with as many as 16 or 32 sockets, are very very difficult. Scaling up is very difficult, and that is why these servers are very very expensive. Why do you think huge SGI servers with 10.000 cores are cheap, and 16 socket Unix servers are very very expensive? You could buy many clusters for the price of one large Unix SMP server. SMP servers are typically serving many 1000 of users, running business software. If you look at the SGI customers on their homepage, none of them are using SGI servers for business ERP software, all Altix/UV servers are used as clusters. For isntance SAP HANA is clustered software.
Clusters can not replace SMP servers on business workloads. That is the reason there is always a market for huge Unix 16/32 socket servers, including Mainframes. There have never been a Linux vendor selling 16/32 SMP servers. Such large Linux SMP servers have never been for sale, they simple have never existed. You are invited to link to a Linux server with 16 or 32 sockets. Good luck finding one. Linux can not scale to 16/32 socket servers, because the hardware has never existed. Of course, people have compiled Linux onto large Unix servers, such as IBM P795 / HP Unix Integrity 64 socket server (google Linux Big Tux) - but the scaling is awful. Nobody buys a huge Unix 16 socket server for many millions to run Linux.
In short, SGI clusters are never used for SMP business workloads, just look at the use cases. They are exclusively used for clustered workloads.
SGI and ScaleMP (another Linux vendor with 10.000s of cores) explains this best. Both of these servers use a single image Linux kernel. Here they say that SGI and ScaleMP are only used for clustered workloads, and their huge Linux servers can not replace a huge 16 socket Unix server (because clusters can not be used for SMP workloads):
http://www.realworldtech.com/sgi-interview/6/
"...The success of Altix systems in the high performance computing market are a very positive sign for both Linux and Itanium. Clearly, the popularity of large processor count Altix systems dispels any notions of whether Linux is a scalable OS for scientific applications. Linux is quite popular for HPC and will continue to remain so in the future,
...
However, scientific applications (HPC) have very different operating characteristics from commercial applications (SMP). Typically, much of the work in scientific code is done inside loops, whereas commercial applications, such as database or ERP software are far more branch intensive. This makes the memory hierarchy more important, particularly the latency to main memory. Whether Linux can scale well with a SMP workload is an open question. However, there is no doubt that with each passing month, the scalability in such environments will improve. Unfortunately, SGI has no plans to move into this SMP market, at this point in time..."
http://www.theregister.co.uk/2011/09/20/scalemp_supports_amd_opterons/
"Since its founding in 2003, ScaleMP has tried a different approach. Instead of using special ASICs and interconnection protocols to lash together multiple server modes together into a SMP shared memory system, ScaleMP cooked up a special software hypervisor layer, called vSMP, that rides atop the x64 processors, memory controllers, and I/O controllers in multiple server nodes....vSMP takes multiple physical servers and – using InfiniBand as a backplane interconnect – makes them look like a giant virtual SMP server with a shared memory space. vSMP has its limits.
...
The vSMP hypervisor that glues systems together is not for every workload, but on workloads where there is a lot of message passing between server nodes – financial modeling, supercomputing, data analytics, and similar parallel workloads. Shai Fultheim, the company's founder and chief executive officer, says ScaleMP has over 300 customers now. "We focused on HPC as the low-hanging fruit "
.
Here is another discussion on huge scale-out clusters can never replace 16 socket SMP servers for enterprise business workloads:
https://news.ycombinator.com/item?id=8175726
"I'm not saying that Oracle hardware or software is the solution, but "scaling-out" is incredibly difficult in transaction processing. I worked at a mid-size tech company with what I imagine was a fairly typical workload, and we spent a ton of money on database hardware because it would have been either incredibly complicated or slow to maintain data integrity across multiple machines. I imagine that situation is fairly common.
...
Generally it's just that it's really difficult to do it right. Sometime's it's impossible. It's often loads more work (which can be hard to debug). Furthermore, it's frequently not even an advantage. Have a read of https://research.microsoft.com/pubs/163083/hotcbp12%20final.... Remember corporate workloads frequently have very different requirements than consumer."
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