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8

The Pag├Ęs-Wilbertz paper is a very good one. To answer more directly to you underlying question that is: "in which quant finance area to use hardware acceleration?"; the points to take into account are: GPU is very good for parallel computations (already underlined in remarks) but bad for memory sharing between the master software and the GPU-hosted ...


8

The very good description of specialized hardware in finance can be found at Cisco.com - Algo Speed High Frequency Trading Solution section. Their High-Performance Trading Architecture (pdf) poster is just great to find out used hardware for different purposes and there are also some presentations, white papers and videos about Cisco's solutions for ...


6

The only real use of this type of hardware in trading that I've seen is the recent spate of FPGA-based risk engines and feed handlers. See this article for some pointers; googling for some obvious keywords will provide more. Given the very small deployment volumes, it seems unlikely that anyone would be looking at ASICs for this.


6

Some examples: Exegy's ticker plant uses FPGAs and InfiniBand. Redline Trading's ticker plant is packaged as a PCI card and uses the IBM Cell Processor. SolarFlare makes a line of 1G/10G nics that are heavily used because they also ship an alternate POSIX-compatible socket API that bypasses the kernel and uses DMA for reduced latency. There are surely ...


6

Coming from an HPC background myself, I know too well the feeling of owning a hammer and yet having no nail. Your question is about computational bottlenecks that can be relieved with GPGPU, though I'm afraid to admit that there aren't many in finance. For realtime applications, the network is the bottleneck; for historical applications, the memory is the ...


6

If you're writing a "highly-optimized" book then you should be tailoring that book to the venues from which you will be receiving data. Max price, for example, is published in NASDAQ's Itch spec: 200,000.0000. If you plan on trading US equities you better go read each of the venues depth of book specs very carefully. You'll find all sorts of ways to ...


4

A correct answer would depend on the instruments and markets you're trading, and whether this is for handling public or propietary orders. For example, if I were to design for the simple case, US equities and a public market, I'd want the queue size at each price level to be able to handle at least the maximum daily volume of, say, QQQ. I know that's in no ...


4

There is a very small minority of people using nvidia GPGPUs which can be programmed with the CUDA libraries. This sort of specialist hardware can be very effective at solving certain problems - mostly where you have very little I/O. More generally, if you are interested in how people are using GPGPUs, then I recommend taking a look at this question on ...


3

There are few surveys atm as people are still relatively secretive about it because of the various challenges a production system poses. Actually a major bank even backstepped after some initial efforts. So there is now quite some activity in the field but not so much as the initial hype suggested. You can also try asking in the dedicated Linkedin group. ...


2

In exotics options pricing, there are lots of CPU bottlenecks -- for example the calculation of Fast Fourier Transform or Monte Carlo simulation. When I price a range accrual in Libor Market Model, I don't use a lot of data (carefully optimized, everything should fit in a few MB of L2 cache), but I do a lot of calculations. This is where, I think, a GPU may ...


2

In regards to size: I assume that you would most likely want to structure the software to implement each 'book' as a unit of orders representing that particular security, and possibly (as mentioned above) further divide these structures into individual pricing structures such-structures. You would want to be able to scale each individual 'unit' to a pretty ...


1

That depends on your application, obviously. If you intend to run Matlab or Python on a single machine, and you're looking into which graphics card to buy, multiplication vs addition should not matter much. I that situation I would recommend an Nvidia card which features CUDA. For CUDA, there are lot of libraries available which make it easy to adapt ...



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