About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

Cantor Evaluating Calxeda ARM Chips for 10x Breakthrough

Subscribe to our newsletter

“I think the Calxeda-ARM machine is an exciting step … I’m evaluating carefully how it can impact the metrics I care about,” says Niall Dalton, director of high frequency trading at Cantor Fitzgerald. He is referring to today’s announcement by Calxeda of their very low power microprocessors based on the ARM architecture – and HP’s plan to build servers based on them.

ARM-based chips run on very low power, and are used by many manufacturers of consumer devices, such as mobile phones. Austin, Texas-based Calxeda is, however, building its chips for highly parallel server designs.

The initial EnergyCore processor – or Server on a Chip – from Calxeda includes four ARM cores, 4MB of L2 cache memory, an 80 gigabit per second interconnect and system/power management functions – all requiring just 1.5 watts of power.

HP will build servers with 288 EnergyCores in a 4U appliance. “A single rack of HP’s Calxeda servers delivers the throughput of some 700 traditional servers and dramatically simplifies the infrastructure needed to hook them all together and manage the cluster,” claims Calxeda co-founder and CEO Barry Evans.

“Companies in our industry are constrained by space and power, yet our appetite for analysis is insatiable,” says Cantor’s Dalton, who continues: “We need a 10x breakthrough and this could be it. We are evaluating the Calxeda technology in hyperscale throughput computing for data and simulation intensive applications. The Calxeda Linux platform enables rapid porting of our software, enabling us to quickly leverage the energy-efficient ARM cores and Calxeda’s scalable communications fabric to scale our applications to new heights.”

For financial markets applications, it looks like Calxeda’s performance/power footprint could be a winner for those firms needing to mine data to develop pre-trade models and post-trade simulations – as fast as possible.  And where those systems are in outsourced managed environments, and possibly in proximity and co-lo centres, the operations costs related to space and power can be considerable.
 

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: Data platform modernisation: Best practice approaches for unifying data, real time data and automated processing

Financial institutions are evolving their data platform modernisation programmes, moving beyond data-for-cloud capabilities and increasingly towards artificial intelligence-readiness. This has shifted the data management focus in the direction of data unification, real-time delivery and automated governance. The drivers of this transition are improved operational efficiency as manual processes are replaced by faster, more accurate automated...

BLOG

Bridging the Data Monetisation Gap

The strategic argument for treating market data as a product rather than a cost has arguably been won. What remains stubbornly unresolved is what comes next: measuring the return on data investments, breaking the hoarding cultures that prevent data from flowing across the enterprise, and building infrastructure robust enough to support AI at scale. Those...

EVENT

AI in Data Management Summit New York City

Following the success of the 15th Data Management Summit NYC, A-Team Group are excited to announce our new event: AI in Data Management Summit NYC!

GUIDE

Regulatory Reporting Handbook – First Edition

Welcome to the inaugural edition of A-Team Group’s Regulatory Reporting Handbook, a comprehensive guide to reporting obligations that must be fulfilled by financial institutions on a global basis. The handbook reviews not only the current state of play within the regulatory reporting space, but also looks ahead to identify how institutions should be preparing for...