The thread we started to determine if it made sense to rewrite an optimized Credit derivative P&L and Risk batch (here) can be wrapped up. It is not worth the time to optimize the code. If you need fast Gaussian Copula computing, go make a deal with Maxeler, they have already got one , you see? If you need a fast contemporary credit derivative batch (CDS, CDX,  ITRX) you can probably just optimize your conventional serial code and be competitive in 3-6 months. We have shown how to optimize the code in these posts, if you do not already know how to proceed.   It would make sense to evaluate Maxeler and the dataflow architecture idea but check your wallet if someone starts doing that “equivalent to 12,000 x86 core dance” for a given computation unless you are reasonably familiar with the code. We have no problem with the concept that Maxeler may be significantly faster, even for CDS valaution and risk, than a tightly optimized off the shelf code (MJ Flynn is a hitter with a record in computation circles) but we haven’t seen the argument yet (and we doubt it is a simple argument). Moreover, why does dataflow payoff architecturally now for Flynn when it did not payoff for Arvind for like 20 years ending in Monsoon?

From a Credit trading perspective, realtime or otherwise, there is nothing to see here, just move on.

From a macro risk perspective it is unambiguously a good thing to have a fast view of  the aggregate risk across single name, index, and correlation inventory. Getting solid analytic numbers for a old piece of quant code is notoriously hard. No one with actual market background will check market convention assumptions, trade representations , or the valuations.  If some enterprising company  has a tied out version of the old quant code get it from them.

But there is more to this story. If the broker dealer cannot bring themselves to separate the correlation book from the credit book arguing that the correlation book is managed to zero risk and standard reserves are held  while the trades roll off. Then in order to run second order risks like counterparty valuation adjustment, the fast correlation risk becomes important. Unless you get the correlation book running to speed your second order risk monte carlo simulation batches performance will be dominated by the correlation book performance (assuming every other product book batch demonstrates competitive performance and better turnaround time compared to the correlation book). Hello, Maxeler.

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