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The Pagès-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 ...


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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. ...


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You should write some kernel functions in CUDA (Nvidia language) for your matlab code. Arrayfun is quite restrictive and not appropriate. Look at this link http://fr.mathworks.com/help/distcomp/run-cuda-or-ptx-code-on-gpu.html for more details about matlab and parallel computing.


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There are some restrictions to using arrayfun. You can read the restrictions here. Judging from the error, you cannot use indexes the way you are. You probably have to create separate GPU arrays for $ V_{t+1} $ and $V_t$. I suggest that you find similar examples in Matlab's website and try to replicate its functionality. Here is an article with ...


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It's been a few years since the OP, and GPU usage is much more common. While still experimental, most institutions we talk to are running GPUs in the data center in some capacity. GPUs are good at large aggregations and chewing through large and streaming datasets which translates to things like: x-Valuation Adjustments (xVA) in relation to derivative ...


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