While the impact has already been enormous, history will show how the shift from floor-based specialist trading to electronic trading changed the way investors, specialists, investment banks, brokers, exchanges and other industry participants make their money. Wall Street as a whole is now firmly entrenched in this new electronic trading frontier and the barriers to entry have shifted from the human imperfections of floor based traders or specialists, to the high-speed, low latency capabilities of profit seeking electronic algorithms.
Low latency in the scope of electronic trading refers to the utilization of high-performance technology that collapses the time between price discovery (i.e. 100 shares of IBM are now available at $100.00) and the execution of orders (i.e. buy or sell) at the newly discovered price. Electronic trading has created a world where the lifecycle of price discovery to trade execution is on the order of single-digit milliseconds.
Time is Money
Previously, I talked about the bandwidth problem. Inability to handle the required bandwidth utilizations of modern market data feeds will certainly cause significant delays in this millisecond-sensitive trade lifecycle, resulting in lost profits. However, the single most important need that has resulted from the unanimous shift to electronic trading is the need for speed, where speed refers to the ability to “see” stock prices as quickly as they appear in the electronic marketplace and similarly the ability to immediately trade on that price before competitors do
Some of the low-latency design strategies or techniques exhibit the elegant characteristic of solving the bandwidth problem as well as the need for speed. For example, colocating your electronic trading algorithm in the same facility as an exchange’s matching engines (i.e. the systems that execute the buy/sell orders) will not only save your firm the wide-area network infrastructure required to feed market data to your trading algorithm, but will also minimize the propagation delays between market data sources and execution venues. Incredibly, some of these solutions, such as FAST compression, can theoretically address the bandwidth problem, the need for speed, and the storage dilemma.
How does Wall Street solve the need for speed? Here are just some of the approaches used to minimize stock trading related delays:
Chip Level Multiprocessors (CMP)
When Intel’s microprocessors started melting because of excessive heat, the multi-core chip industry became mainstream. Smaller multiple cores on a single chip could now permit multi-threaded code to achieve true parallelism while collapsing the time it takes to complete processing tasks. Multi-core chips from Intel and AMD have a strong presence in the capital markets and can achieve remarkable performance as shown in SPEC benchmarks.
An emerging challenge on Wall Street is to deploy microprocessor architectures capable of scaling to the enormous processing required by risk-modeling and algorithmic-trading solutions. If one-core architectures encountered space and heat limitations which eventually lead to the introduction of multi-core architectures, what new limitations will emerge? The shared message bus found with existing multi-core processors is one such limitation as the number of cores multiply. Vendors, such as Tilera are innovating around these limitations and you can expect more to follow. Furthermore, evidence is building to support the notion that multi-core microprocessor architectures, and the threading model behind them are inherently flawed. Multi-core CPUs may provide near term flexibility for designers and engineers looking to tap more processing power from a single machine. Long term however, they may be doing more harm then good.
With multiple cores now in place, the software and hardware community are steadily catching up. For example, older versions of Microsoft’s Network Driver Interface Specification (NDIS) would limit protocol processing to a single CPU. NDIS 6.0 introduced a new feature called Receive Side Scaling (RSS) which enables message processing from the NIC to be distributed across the multiple cores on the host server.
As Herb Sutter explains in his paper “The Free Lunch is Over: A Fundamental turn Towards Concurrency in Software”, software applications will increasingly need to be concurrent if they want to exploit CPU throughput gains. The problem is that concurrency remains a challenge from an education and training perspective as described in David A. Patterson paper. Conceptually concurrency can drive the need for speed. The practice of this approach remains a challenging one.
Colocation is a fascinating approach towards achieving low-latency, mainly because it reconfigures physical proximity between application stacks instead of relying on a sophisticated technology approach. We’ve already shown how it can minimize the bandwidth requirements for a firm’s algorithmic trading platforms, but its biggest accomplishment is to minimize the distance between electronic trading platforms and the systems that execute the trades. Organizations such as BT Radianz have armed their high-performance datacenters with the fastest, highest throughput technology on the planet. When coupled with colocated hosting services, these data centers provide the the lowest latency money can buy while opening up new opportunities to translate this value throughout the application stack starting at the NIC card and moving on up.
The Exchanges themselves, are also using colocation services as a way to attract customers and introduce new sources of revenue. For example, International Securities Exchange ISE, offers colocation services while promising 200 microsecond service levels.
Field Programmable Gate Arrays
The name says it all – an integrated circuit that can be customized for a specific solution domain. Specialized coprocessors have existed for years, handling floating point calculations, video processing and other processing intensive tasks. FPGA builds on this by offering design tools allow programmers to customize the behavior of the FPGA’s integrated circuit, usually through a high-level programming language which is then “compiled” into the board itself. An example of how FPGA boards are being deployed on wall street includes replacing software feed handlers, the components that read, transform and route market data feeds, with their FPGA equivalents. This approach results in higher throughput and lower latency because message processing is handled by the customized FPGA board, instead of the host CPU/OS, saving the precious cycles that would have been required for moving messages up the protocol stack and interrupting the kernel. ACTIV Fiancial, a leading vendor of a feed handling solution claims that the introduction of FPGA accelerators to their feed processing platform reduced the feed processing latency by a factor of ten while allowing them to reduce the servers required to process some US market data feeds from 12 servers, in the software based feed processing approach, to just one server in the FPGA accelerated approach. Celoxica is another firm specializing in FPGA solutions for Wall Street’s electronic trading. Celoxica’s hardware accelerated trading solution promises microsecond latency between host NIC and user application with support for throughput rates reaching 7 million messages per second.
TCP Offload Engine
The idea with TCP Offload Engines (TOE) is for the host operating system to offload processing of TCP messages to hardware located on the network interface card itself, thus decreasing CPU utilization while increasing outbound throughput. Windows 2003 Server includes the Chimney Offload architecture which defines the hooks required for OEM and 3rd party hardware vendors to implement layer 1, 2, 3 and 4 of the OSI protocol stack in the NIC itself, before passing the message to the host operating system’s protocol handlers. Similar examples of offload technology include TCP Segmentation Offload (TSO) or Generic Segmentation Offload (GSO) where the NIC handles the segmenting of large blocks of data into packets.
Network Processing Offload
High-Performance Interconnections (I/O)
From the Infiniband Trade Association website:
In 1999, two competing input/output (I/O) standards called Future I/O (developed by Compaq, IBM and Hewlett-Packard) and Next Generation I/O (developed by Intel, Microsoft and Sun) merged into a unified I/O standard called InfiniBand. InfiniBand is an industry-standard specification that defines an input/output architecture used to interconnect servers, communications infrastructure equipment, storage and embedded systems. InfiniBand is a true fabric architecture that leverages switched, point-to-point channels with data transfers up to 120 gigabits per second, both in chassis backplane applications as well as through external copper and optical fiber connections.
Infiniband technologies also exhibit the characteristic of solving multiple problems facing Wall Street today including bandwidth, latency, efficiency, reliability and data integrity. Visit the Voltaire website for a vendor specific look into the performance benefits of Infiniband on Wall Street.
Please check back in second quarter 2008 when The Techdoer Times presents a detailed look into the many existing and future applications of Infiniband technology.
Remote Direct Memory Access
RDMA is a zero-copy protocol specification for transferring data between memory modules of separate computers without involving either source or target operating sytem or CPU, resulting in low-latency and high-throughput computing.
Gigabit Ethernet (GbE) & 10 Gigabit Ethernet (10GbE)
AMD HyperTransport (Chip-level)
Intel Common System Interface (Chip-level)
Ethernet Virtual Private Line (EVPL) and Ethernet Virtual Connection (EVC)
As we mentioned in the bandwidth problem, some firms are relying on innovations in compression as a way to minimize escalating bandwidth costs. FAST is an example of this but there’s more. In our previous postings on measuring the latency in messaging systems we explained how the different components of latency react to variations in packet size or transmission rates. Herein lies the potential latency improvements resulting from the adoption of FAST. FAST can potentially minimize packetization and serialization delays. It is true that the process of compressing messages requires additional CPU cycles and therefore adds to the application delay, however, depending on the nature of the solution, this additional delay may be offset by the savings that result from serializing significantly smaller sized packets onto the wire, potentially 80% smaller. FAST can be incredibly effective at bandwidth reduction and can potentially reduce end-to-end latency as well.
Messaging technology has evolved greatly to the point where requirements for speed and reliability are no longer in conflict. Publish/Subscribe messaging paradigms can be supported with different levels of service quality, ensuring that latency-sensitive subscribers can forgo message recovery for the sake of speed, while data-completeness sensitive subscribers can rely on extremely fast message recovery built on top of layer 3 protocol and routing technologies such as UDP and Multicast. These real-time messaging technologies also ensure robustness and scalability across a number of downstream subscribers. Cases where slow subscribers begin to “scream” for message retransmission (aka. ‘crying-baby’) can be handled individually and gracefully by the messaging layer, ensuring uninterrupted service to other subscribers. Messaging technology vendors include:
As mentioned in the bandwidth problem, multicast routing technologies can potentially reduce latency in addition to bandwidth utilization. The latency play results from the fact that multicast packets are rejected or accepted at the Network Interface Card (NIC) level, and not the more CPU expensive kernel level.
Data Grids/Compute Grids
With the industry’s reliance on the timely evaluation of strategic trading and risk models comes the need to access and crunch large amounts of data efficiently. This reliance has spawned innovations in the form of data and compute grids which offer highly-resilient, scalable distributed processing infrastructure on demand for compute intensive as well as data intensive environments. Data grids, in particular, offer a high-performance, highly-resilient middle-tier data layer that sits on top of storage technologies and other information sources but offers ubiquitous data access to enterprise business processes. Key vendors or technologies in this space include the following:
Collapsing Distributed Processing
Yet another approach to decreasing the overall end-to-end latency of messaging systems is to collapse the ends, which also minimizes the propagation delays. The closer each distributed processing node is to being within the same process of dependent nodes, the better the overall performance. The rise of Direct Market Access (DMA) approaches where firms connect directly to the exchanges and other providers of market data, instead of third party vendors of the data is an example of this. DMA alone spawned a new market data distribution industry with the net result being end-to-end latency for market data measuring in the low milliseconds, which for a while remained faster than the same data distributed by vendors such as Reuters and Bloomberg.
Thus far we’ve shown how firms in the capital markets are confronting their bandwidth problem and need for speed. The third category of challenges is the Storage Dilemma facing these firms.