Tag Archives: financial computing

FPGA-accelerated financial analytics get real

Automated trading and near-real-time financial analytics have been hot topics for some years now. Large organizations such as Bank of America deploy massive compute clusters to do such things as calculate the present value of options, or to model credit derivatives, in a virtual arms race to make trades with ever-higher levels of accuracy and ever-lower latencies. The banks and hedge funds that win this race each day have the potential to make millions or billions of dollars in extra profits. Vast amounts of power are consumed to drive the world’s most advanced supercomputers in a constant quest to produce, well, nothing at all… Just information used to move wealth from one global pants-pocket to another.

And all in the pursuit of market efficiency, of course. Hopefully all this money-shuffling is good for my meager retirement portfolio.

Editorializing aside, there has been a lot of buzz about the role of accelerators, including FPGAs, in financial applications. XtremeData this week generated some press regarding their new accelerated database for analytics. Their solution is attractive because it combines an FPGA module with an industry-standard HP Proliant server to accelerate specific algorithms (in this case SQL queries) by 15X over software-only equivalents.

As an industry, we need more turnkey solutions that highlight the benefits of FPGA acceleration. With enough such applications out there, the demand for programming and hardware platform solutions for other, possibly unrelated applications will increase.

Assuming, of course, all this financial alchemy doesn’t once again turn gold into lead.

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A reconfigurable tick squeezer?

This headline caught my attention today:

Vhayu Introduces Hardware Compression for Its Tick Database
Combining Vhayu Velocity with an FPGA, Squeezer compresses data by a factor of four with no performance penalty, says the vendor.

My first thoughts on seeing that headline were:

  • Is there really such a large database on ticks that hardware compression is required?
  • Would somebody actually call such a tool, “Squeezer”?
  • Is this April 1st?

On further reading, however, I realized that “tick” means “ticker”, as in market trading data.

Aha!

In fact, feed handling is a domain in which FPGAs are starting to gain significant traction. It’s all about getting the lowest possible latency: data comes directly into the system from a market feed source, and a hardware-based algorithm makes a split-second trading decision based on observed patterns. A sudden downturn in the price on key indicator stocks, for example. Or a spiking in oil prices, or a drop in the Brazilian Real, or whatever.

The trading house, hedge fund or bank that sees the pattern and reacts first is that one that wins. And so it’s a latency war out there. FPGAs represent one solution to the latency problem, and have been deployed in numerous trading-related market data appliances.

I like this quote attributed to Jeff Hudson, Vhayu CEO:

“It’s a hybrid software/hardware world we’re entering now, and those companies that embrace it will prosper and those that don’t will fall way behind.”

Indeed.

See also: High Frequency Traders Get Boost From FPGA Acceleration

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