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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 ...


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 ...


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. ...


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 ...


Claudio Albanese has a paper on the topic of GPUs and CVA computations. Here is one of his papers: link to paper


This book is quite good as a starting point: http://www.amazon.co.uk/Counterparty-Credit-Risk-Challenge-Financial/dp/047068576X

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