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To Grid Or Not To Grid?
dbgimp writes "In my job at a (large) investment bank I am constantly being pushed to use grid technology.
I have many problems with this (not least that our data center is at best 100 Mb/s and our software is actually more data than computation heavy). A typical batch job takes 10-30 minutes consisting of around 10,000 trades. I would far rather spend the time and money on multi-core machines and optimizing the software than on the latest fad technology.
I am interested to hear from other people in a similar position and, in particular, why or why not they chose grid software over improving the existing code to leverage better processor technology, and which grid software they chose to use and why. Or, conversely, why they chose not to use grid software."
Well, I'm not sure about what your particular job is but my current job is developing webservices. There are two servers that I use, a clustered and an unclustered. I deploy the same projects to them--and occasionally find myself rectifying strange resource allocation problems on the clustered server. There's only two machines on that cluster so it's more symbolism right now to the consumer that our software is scalable.
That's right, it seems to me that upper management likes the idea of having a clustered system because if a customer ever asked if our software would work for 1,000 people, my manager would say, "Sure, just buy more machines for the cluster." And everyone likes that idea. The idea that well, the system might not be able to handle everyone right away but wait a year or two and CPU cycles will be so cheap we can just buy 30 low end machines and cluster them to get the job done. Thanks to the common scheme of access that all databases use, this is an actual option.
I offer only the suggestion that maybe your bosses like the idea of just being able to throw more machines at it. Look at it from a financial perspective, if you tailored the code for multi-core CPUs--something I'm not even sure how to do--you would have to rebuild and maybe recode everything for future generations of machines. I can see why grid computing might sound so enticing to your employer. Look at Google's distributed scheme, hundreds of thousands of cheap machines running a stripped down form of Red Hat--I don't know if that's 'grid' computing but I imagine it's along the same lines.
It isn't clear to me whether your bank offers a service for trading or you do them in batches. It seems that the latter is true. Now, you mentioned you work at an investment bank so money probably isn't that big of an issue. Just go to your superior and say, "Look, I need the following." and if he balks at you just ask him how important these 10,000 transactions a day are to him.
So, to me, it would seem more intelligent to use the following idea. Buy new network hardware that handle gigabit ethernet. The cards, the router, whatever you have, just up it so that your internal network can really throw data around. Maybe look at relaying fibre if you don't have it. Then take what money is left over and buy a few more machines. Get a low-end server to act as a proxy that dishes out the requests for a trade to a cluster of machines. Write the software independent of the hardware so that you can always just buy more machines and install your client application on the machines. At some point, your choke point is going to be your database but if you make it that far, you've kind of hit a wall, in my opinion, and the only solution for that is to juice up the box (with database sepecific hardware) that's serving your database.
My work here is dung.
I would say give them the full price to get it done right then have management decide. Be Sure to give them the full quote with the cost of making 10gbs Network or faster for these systems. Then you need to realize how much of the code can be parallelized, estimate you time it will take you to make the changes, and add in proper debugging time. Next find alternative solutions that will increase performance. For example except for grids you have clustering which works better for some applications which have fast calculations but a lot of users that just make it slow. Grid Computing works well when you have a large segments of code with minimum communication between each system. If you need a lot of CPU to CPU communication then you will need to get a real supercomputer where the processors communicate across the bus.
Or You just need to index your tables.
The rule of thumb is go the safe rout unless you are told by higher ups to do otherwise, have higher ups sign off on the more risky method (to save your butt) then get the method working focusing on getting the job done right and stop complaining how bad decision it was.
If something is so important that you feel the need to post it on the internet... It probably isn't that important.
Maybe it's just me, but you sound like you're just resisting going to a different technology where you have to learn a new set of skills. Grid/multi-processor computing is definitely not simple, but depending on how many spare CPU cycles you have, you'll get a much faster and larger speedup in your runs than if you tinker with the code to make it run faster single-threaded. Also, won't you need to do some multi-threaded on a multi-core machine as well? (I'm not particularly familiar with multi-cores, so I could be wrong).
I recommend learning how to use grid computing and convert the program. Not only will your code run faster at the end of the day, you'll gain a valuable set of skills that will look great on a resume.
If the process is more data than computation intensive then throwing more machines at the problem is the most cost efficient way of going forward. You have already countered your argument for multi-core machines. Especially if this is finance it is highly unlikely that optimizing the software will produce anything remotely practical in a short time period or at low cost. Software optimization also can introduce bugs and lock you down on an implementation that cannot be easily updated.
Take search engine technology as an example, Google have hundreds of thousands of machines running advanced software on non ultra-optimized platforms: Java and Python. The alternative is having a couple of hundred big iron machines running hand tweaked C / assembly. As a business you should be seeking to reduce the overhead of operations, by increasing the number of machines, lowering the cost of each machine, reducing the time optimizing the software by allowing higher level languages that are easier to use and maintain you can actually get better performance, reliability, and flexibility.
Period.
In fact, GRID software is constantly in flux, because there is no grant money to run a GRID, only to develop one, so they keep throwing stuff out and developing new parts -- to get grant money.
And yes, I am posting this anonymously because I work for such a place, and mostly like my job.
Make sure you know the difference between grid technology and clustering. Basically, grid is much more complicated but more flexible; the name means you can connect something to a grid to get computing power, just like you can connect to the power grid to get electricity. It looks like you're thinking of clustering instead, which is easier to deploy and in many ways closer to a multiproc machine
Escher was the first MC and Giger invented the HR department.
I can't help but feel that people are missing the point of grid computing. Grid is not HPC. It's not super computers. You can build grids using HPC, but they don't have to go hand in hand. As such, all this talk about parallel whatnot is actually missing the point. I assume there exists code. I assume the code is serial, since most is. I also assume that there are many of these jobs, rather than one job that currently takes a day and a half. Typically there's no need to start getting exotic with MPI/OpenMP or whatever. Simply submit more serial jobs to do what needs to be done. Look at it from a batch scheduling point of view, and see what can be done. If you want to parallelise it as well feel free.
Grid within a company typically just means decent remote access to a shared cluster. A web service that submits jobs to sun grid engine (which has nothing to do with 'grid' btw) would probably fill in all the buzzword bingo requirements of a grid project without being anything of the sort. For sadists look into OMII and GT4, but don't feel compelled...
jh
Our processes tend to be more computation (than data) heavy compared to what you describe, but we are using lots of clustered computers. Take your 10,000 trades and split them into chunks of 100 trades and have separate machines value each chunk and reassemble the results. Depending on the nature of what your software does this may or may not make sense. If you can split your workload into small chunks that can be analyzed independently you can achieve much better throughput.
The newer cluster/grid software can be really shiny, but you don't always need it. Plain old PVM can still work wonders. Also, a lot of the commercial cluster software out there isn't well suited to this kind of high performance computation clustering.
My management is similarly obsessed with virtualization: they want to lower admin costs, lower lab costs, etc through this simple technology.
So, rather than move everything over to lpars I took a simple step - purchased a large virtualization-oriented server highly touted as perfect for this, and moved over a single app, with the goal of putting two apps on this server. Along the way I learned:
- io virtualization sucks for io-heavy applications
- the tools to determine how much of the cpu your app is getting at a given moment stink
- memory virtualization in which you resize application memory is primitive and almost useless
- there were no guidelines for optimization of the server - just recommendations to try it
hundreds of different ways and leave it on the best settings
- basic setup of the machine required wading through tons of jargon that even the os engineers didn't seem to know well
- out of the box - a single app on the new virtualization server performed more slowly than it did on a free seven year-old server
- some of the most heavily-advertised virtualization features of the product just don't work
- virtualization of multiple busy apps onto the same server is mostly a waste of money
- virtualization of multiple mostly idle app (failover servers, test servers, demo servers, etc) should work very well
- we spent at least $25k on labor just to create something that was a slam dunk
- I'm glad that we started with a small prototype - and didn't waste a ton of cash moving everything over immediately the way some management hoped
- I think in the end we'll get multiple apps working on this box just fine. BUT - we will have spent more money on this scenario than by simply purchasing separate systems. We may recoup a savings if we move enough idle systems onto virtual boxes.
As a result of this experience my team now knows more about virtualization than any other people in the division, we now have a production server supporting it, my management is now cool on this technology, and there is no risk of being forced to migrate critical servers over quickly to the virtual world. I'd call that a success.
I think that you're right - that grid is in a hype cycle right now. So - there are quite a few disappointments to be had along the way to its implementation. For example - if your workload is heavily transactional - you're really not going to get much benefit. In this example oracle supports grids - but it is really more about failover than performance. If you roll your own or use a more sophisticated product you can be safe in assuming that you'll hit unexpected issues, a gap between vendor marketecture & what you really need, and possibly the pain of having a vendor talking directly to your management.
You might want to consider having management fund a small prototype to prove out the benefits. Then let them see that they can achive perhaps better availability but worse performance at a very high cost through this approach.
good luck
Sounds like a trade-auditing project I was once on.
If the 10,000 trades are easily broken into small groups, such as by the initial letter of the ticker symbol, and if all the data for the analysis is fetched in the first step, you can in fact spread the processing over 26-odd machines for a speedup of (fixed part + (per-ticker-symbol part/26)).
I have an article on doing the load-balancing part of this kind of processing, albeit on a large multiprocessor, at http://www.sun.com/blueprints/0605/819-2888.pdf[In PDF].
As you've already guessed, sometimes the problem doesn't decompose
nicely into parts that can be distributed to machines
far from the database.
The rule of the thumb is that grid does distributed computation, where you ship small amounts of data to many CPUs. If you have large amounts of data, you need to have previously distributed data stores, and then you ship the processing to reside with it, instead of the other way around. Alas, some folks call the latter grid, when it should be called something like "data grid" (;-))
--dave
davecb@spamcop.net
Something you might want to experiment with is Quantian. It is a bootable linux distro (knoppix descendant) with clustering (openmosix) and a huge variety cluster capable scientific & financial open source tools built in. It is a very quick & easy way to set up a cluster to experiment and see how you application could be altered to work well in a massively parallel environment. I've never seen a quicker or easier way of building a cluster. With Quantian and a pile of networked PCs, you can literally have a openmosix cluster in minutes.
7. It's not a fad. The technology has been around since the 80s IIRC, possibly earlier. The word "grid" is a fad, but not the technology. They started as network or batch queueing systems. The word "grid" is like the word "middleware". It isn't well defined and means a bunch of different things to different people.
8. Off the top of my head, freebies include Torque, GridEngine, Condor.
9. Yes it would be a Beowulf of those. Mwhahaha!
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