モンテカルロシミュレーションにnvidiaGPUを使用して調査しています。ただし、gsl乱数ジェネレーターと、SPRNGなどの並列乱数ジェネレーターを使用したいと思います。これが可能かどうか誰かが知っていますか？
アップデート
GPUを使用してRNGで遊んだことがあります。現在、良い解決策はありません。SDKに付属しているMersenneTwisterは、シードの生成に非常に長い時間がかかるため、（私の）モンテカルロシミュレーションにはあまり適していません。
NAGライブラリはより有望です。RNは、バッチまたは個別のスレッドで生成できます。ただし、現在サポートされているディストリビューションは、均一、指数、通常の一部のみです。
The GSL manual recommends the Mersenne Twister.
The Mersenne Twister authors have a version for Nvidia GPUs. I looked into porting this to the R package gputools but found that I needed excessively large number of draws (millions, I think) before the combination of 'generate of GPU and make available to R' was faster than just drawing in R (using only the CPU).
It really is a computation / communication tradeoff.
My colleagues and I have a preprint, to appear in the SC11 conference that revisits an alternative technique for generating random numbers that is well-suited to GPUs. The idea is that the nth random number is:
x_n = f(n)
In contrast to the conventional approach where
x_n = f(x_{n-1})
Source code is available, which implements several different generators. offering 2^64 or more streams, each with periods of 2^128 or more. All pass a wide assortment of tests (the TestU01 Crush and BigCrush suites) of both intra-stream and inter-stream statistical independence. The library also includes adapters that allow you to use our generators in a GSL framework.
Massive parallel random generation as you need it for GPUs is a difficult problem. This is an active research topic. You really have to be careful not only to have a good sequential random generator (these you find in the literature) but something that guarantees that they are independent. Pairwise independence is not sufficient for a good Monte Carlo simulation. AFAIK there is no good public domain code available.
I've just found that NAG provide some RNG routines. These libraries are free for academics.
Use the Mersenne Twister PRNG, as provided in the CUDA SDK.
Here we use sobol sequences on the GPUs.
自分で実装する必要があります。