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In fact, initially JP Morgan looked at GPUs for acceleration. They ported one of their models to the graphics architecture and were able to get a 14- to 15-fold

by Cieplak 14y ago

In fact, initially JP Morgan looked at GPUs for acceleration. They ported one of their models to the graphics architecture and were able to get a 14- to 15-fold performance boost. But they thought they could do even better with FPGAs. The problem was that it was going to take about 6 months for an initial port. That's when they went to Maxeler and initiated a proof-of-concept engagement with them. http://www.hpcwire.com/hpcwire/2011-07-13/jp_morgan_buys_into_fpga_supercomputing.html http://www.hpcwire.com/hpcwire/2011-07-13/jp_morgan_buys_int...

groby_b 14y ago
Color me skeptical. The FPGA version was basically a rewrite, removing all kinds of OO overhead (objects/classes, templates, etc.) I would not at all be surprised that if that cleaned up code was ported to a GPU, you'd see a much bigger speedup. And if they did this for the GPU code, I'm not quite sure why they needed a consultant to do the same thing before starting to work on FPGA code. I'm not saying the numbers are wrong, but this is certainly insufficient data for any decision :)
- marshray 14y ago
  GPUs [...] were able to get a 14- to 15-fold performance boost [...] Their Monte Carlo model, for example, was able to realize a 260- to 280-fold speedup using FPGA acceleration. These numbers are consistent with other computations I've seen that were well-suited for FPGAs.
  - groby_b 14y ago
    14x seemed awfully small. Looking at e.g. http://mcx.sourceforge.net/cgi-bin/index.cgi http://mcx.sourceforge.net/cgi-bin/index.cgi, they see a ~300x speedup for a monte carlo simulation on the GPU. (Note - it might well be that the FPGA version of that particular problem would be even faster. I merely posted the GP to point out that there was significantly more effort to tailor the model towards the FPGA than towards the GPU, which seemed to be merely a "compile for GPU" without a restructuring to make the data model fit.)
    - marshray 14y ago
      I've also heard that financial companies needed good double precision support, which was not always fully performant on GPUs.

by Cieplak 14y ago

groby_b 14y ago

Color me skeptical. The FPGA version was basically a rewrite, removing all kinds of OO overhead (objects/classes, templates, etc.) I would not at all be surprised that if that cleaned up code was ported to a GPU, you'd see a much bigger speedup. And if they did this for the GPU code, I'm not quite sure why they needed a consultant to do the same thing before starting to work on FPGA code. I'm not saying the numbers are wrong, but this is certainly insufficient data for any decision :)

marshray 14y ago
GPUs [...] were able to get a 14- to 15-fold performance boost [...] Their Monte Carlo model, for example, was able to realize a 260- to 280-fold speedup using FPGA acceleration. These numbers are consistent with other computations I've seen that were well-suited for FPGAs.
- groby_b 14y ago
  14x seemed awfully small. Looking at e.g. http://mcx.sourceforge.net/cgi-bin/index.cgi http://mcx.sourceforge.net/cgi-bin/index.cgi, they see a ~300x speedup for a monte carlo simulation on the GPU. (Note - it might well be that the FPGA version of that particular problem would be even faster. I merely posted the GP to point out that there was significantly more effort to tailor the model towards the FPGA than towards the GPU, which seemed to be merely a "compile for GPU" without a restructuring to make the data model fit.)
  - marshray 14y ago
    I've also heard that financial companies needed good double precision support, which was not always fully performant on GPUs.