McObject's Steve Graves looks at how technology is rising to the processing challenge of modern trading.
Technologies derived from the cellular and embedded markets are
trickling into trading systems as quants in pursuit of
ever-greater performance eschew brute force and embrace
crossover tools whose lightweight profiles and high efficiency
are major selling points.
Some examples are ARM processors, Field Programmable Gate
Arrays (FPGAs), and embedded in-memory database systems
(IMDSs). These technologies might have roots in "small" systems
(i.e. embedded applications with limited CPU, memory and OS
resources), but they can help tackle the computationally
intensive challenges of capital markets, and may provide the
next big leap in financial system performance, determinism, and
Both buy and sell-side firms have historically scaled up
processing power or added high performance networking to
achieve incremental gains, but costs have risen and bottlenecks
It’s well established that there are diminishing
returns on these brute force methods, not to mention increasing
data center costs as processors demand more energy and
produce more heat. There are also latency penalties that result
from data movement through networks and staging data from
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