With:
• Pat Aughavin, Senior Business Development Director, Financial Services, AMD
• Vincent Berkhout, Client Engagement Director, COLT
• Michael Cooper, Head of Product Technology, BT Global Financial Services
• Andrew Graham, IT Architect, Financial Markets, IBM UK
• Shawn McAllister, Vice President, Architecture, Solace Systems
• Parm Sangha, Business Development Manager - Financial Services Industry Solutions, Cisco Systems
• Geno Valente, Vice President, Marketing and Sales, XtremeData, Inc
• Nigel Woodward, Head of Financial Services, Europe, Intel

What developments in processing capabilities should firms adopt to support algo/auto trading?

Aughavin: While many companies are investigating parallel programming, they are proceeding methodically because it can be difficult to maintain and support. However, companies recognise the potential of accelerated computing and how it can reduce power consumption and ease infrastructure complexity. In recent months, select companies have launched accelerated computing initiatives which are specifically designed to help technology partners deliver open, flexible and scalable silicon designs. These solutions can significantly boost performance in compute-intensive applications. A key part of such solutions is a stable platform which will help foster dynamic development, enabling technological differentiation that is not economically disruptive at a time when accelerated computing is moving to the mainstream.

Cooper: Alternatives to traditional horizontal and technology upgrade approaches are beginning to emerge that address complex event processing (CEP), capacity and performance requirements. Network-attached compute appliances seek to address processing capacity and performance by offloading processing from existing systems to an optimised appliance. Additionally, some of these appliances mitigate the overheads frequently incurred with platform and technology upgrades by minimising systems modifications and application development. In addition to meeting existing application performance requirements, these applications can service multiple systems while providing significant scalability and capacity for growth. They will also address other issues like power consumption and cooling requirements, are relatively straightforward to deploy and can prolong the life of the existing systems estate. As a consequence they enable new approaches to be developed and new functionality to be supported that would not have been feasible on existing platforms.

Graham: The need to analyse applications to ensure software is designed to exploit multi-core/multi-threaded technology safely is ever more important. A balanced solution stack must always be considered; the old adage that fixing one bottleneck will only move it to another part of the system still holds true. That said, emerging technologies include:
• Offload engines/accelerators to perform XML transformations, security processing acceleration, FIX/FIXML acceleration, market data feeds optimisation, TCP offload engines (TOEs) and hardware devices such as graphics processing units (GPUs), field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs) and cell broadband engines;
• Accelerated migration of applications from 32 to 64 bit hardware and operating systems, to exploit in-memory databases and larger histories;
• Streaming/event-based technologies with the option to perform more complex processing is gaining significant traction, often blended with column orientation over row orientation;
• The need for predictive quality of service across the architecture
• driving real-time java solutions, real-time extensions to Linux, dedicated or highly-managed networks; and
• Daemonless low-latency middleware that exploits true multicast networking.

Parm Sangha, Cisco Systems.

“The organisation needs to be able to monitor data and message flow in order to determine where bottlenecks may be occurring and what to do about them.”

McAllister: Hardware infrastructure solutions combine the best of network and content-processing hardware advances to accelerate data delivery, routing and transformation in support of algorithmic and other applications. These solutions use FPGA, ASIC and network processor-based systems to move content processing into silicon which improves uptime and delivery rates by parallelising processing and eliminating the unpredictability of software on servers. With data volumes increasing exponentially and buy- and sell-side firms struggling with increased complexity, latency and unpredictability in their software infrastructures, hardware solutions can deliver an order of magnitude greater throughput while guaranteeing ultra-low, consistent latency. ...

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