High Finance: Harnessing the Power of HPC
How financial institutions can use high-performance computing to gain a market advantage.
The financial industry engages in many technologically demanding operations: high-frequency trading, real-time analytics and complex financial modeling, as well as sophisticated fraud detection and security processing. Tasks such as these demand cutting-edge computing that can process large data flows, detect patterns, assess risk, project value and drive profit.
High-performance computing (HPC) systems arm financial firms with the resources they need to compete in fiercely competitive markets. Powerful, multicore processors and parallel storage combine with fast, low-latency networks, reducing time to action and enabling more effective analysis. For example, HPC enables instant execution of high-frequency, algorithmic trading to leverage fleeting market opportunities that result in gains. The technology delivers advanced analytics, allowing firms to detect, assess and manage risk factors in complex markets.
Powerful computing solutions help financial firms address heightened competition in the race to identify new market opportunities by enabling data analytics and predictive modeling. These capabilities can also verify transactions in real time, detect anomalous patterns and improve fraud detection.
The ABCs of HPC
Activities such as high-frequency trading, complex simulations and real-time analytics are essential to the success of capital markets brokerages and other financial firms. These institutions rely on HPC systems to gather, parse, analyze and act on vast amounts of data. In a keenly competitive environment, the imperative to bring top-end computing power to bear is greater than ever before.
Among the challenges:
Success or failure in the financial sector can be measured in milliseconds. High-frequency, algorithmic trading relies on high-speed interconnections, ultra-low-latency switches and powerful servers to ensure immediate action.
The global financial crisis of 2008 exposed deep structural flaws and risks in the sector. HPC deployments and advanced analytics allow firms to detect, assess and manage risk factors before they can damage business interests.
Financial firms are in a constant race to identify new market opportunities and gain critical, first-mover advantages. Data analytics and predictive modeling reveal opportunities that might otherwise be missed.
Every financial organization has a target on its back. HPC and data analytics combine to verify transactions in real time, detect anomalous patterns and improve fraud detection.
These challenges are driving the adoption of HPC solutions in the financial sector. In addition to growing activity around trading and options analysis, the research firm IDC cites advancing security solutions and the emergence of customer-facing channel services as key drivers of HPC adoption.
Subsequently, the adoption of this technology means growth in HPC-related spending in the financial sector. According to IDC, total global revenue for the HPC market (including servers, storage, software and services) will increase from $21 billion in 2014 to $31.3 billion by 2019.
From a hardware perspective, HPC build-outs break down into four components:
Multicore processors, low-latency networks and parallel storage infrastructures combine to let clustered HPC servers scale to the most demanding computing tasks. Compact 1U and 2U form factors allow servers such as the HPE Apollo 6000 System to host as many as 144 individual servers in a standard data center rack. Shared power and management infrastructure improve efficiency and reduce operating cost, yielding sharply higher performance within existing data center footprints and thermal envelopes.
Today’s multicore processors provide parallel execution, streamlined memory access and cutting-edge processing techniques. The Intel Xeon E7-8800 family of processors, for example, is built on an advanced 22-nanometer process and incorporates as many as 18 processor cores, large secondary caches and 5.69 billion transistors for powerful performance. Often sitting next to these system CPUs are powerful coprocessors, such as the Intel Xeon Phi and Nvidia Tesla graphics processing unit (GPU). These accelerate specialized floating point, graphics and other computations, enabling robust parallel processing for complex tasks.
High-performance computing is the ultimate team game, employing hundreds or even thousands of rack-hosted servers linked via high-performance InfiniBand and 10 Gigabit Ethernet networks. To squeeze latency out of the environment, specialized hardware such as ultra-low-latency switches, message acceleration appliances and high-performance network monitoring and management tools combine to accelerate, streamline and manage the intense network traffic typical of HPC deployments. Network interface cards (NICs) from Exablaze, Myricom and Solarflare can squeeze round-trip latencies down to nearly a microsecond.
Data analytics requires robust storage. A survey by Research and Markets found that 31 percent of all HPC storage systems contain more than a petabyte of capacity. Parallel file systems running across multiple nodes can erase bottlenecks for large file transfers and ensure performance on even the busiest storage infrastructures and networks. Solid-state disk storage sharply improves response time, throughput and reliability, while hybrid storage arrays position data across tiers of solid-state and spinning media to balance cost and performance. Leading providers include NetApp, EMC, HPE, Hitachi Data Systems and IBM.
HPC addresses a host of core activities in the financial sector, making it a compelling solution for banks, brokerage houses, insurance companies, capital markets and other organizations.
Data Analytics and Predictive Modeling
Data analytics and predictive modeling leverage historical data, statistical algorithms and machine-learning techniques to detect trends and drive insight into likely future outcomes. In short, they help financial firms predict the future and make better decisions about trades, market movements, credit risk and a host of other issues. The algorithms used to achieve these ends are sophisticated and often proprietary, mandating scalable computing infrastructures tuned for high levels of parallelism.
First-mover advantage is a core benefit, enabling high-frequency trading scenarios where profits accrue by leveraging fleeting opportunities before others can execute. Speedy processing and communications serve to improve outcomes, with real-time market analysis, risk analytics and portfolio calculations combining to assess exposure and risk before transactions are finalized. The ability to crunch reams of data yields a host of other benefits as well. Improved trading models and the ability to find new market opportunities combine to drive down cost and improve overall business performance.
Enhanced Risk Management
Success in the financial sector is determined by how well organizations manage risk. Firms must fully understand all the factors in a transaction, investment, customer or strategy — increasingly in real time — before making a decision.
HPC deployments are effective in addressing risk management across credit, market, liquidity and operational risk categories. HPC can be applied to operations such as basis risk computation to address changes in floating rates and their impact on assets and liabilities. It can also be used to evaluate the market or credit risk of a portfolio, estimate default risk by counterparties in derivative contracts and conduct Monte Carlo analyses to assess broad operational risk.
Monte Carlo simulations provide a good example of risk-focused computation that demands the specific capabilities of HPC. These simulations perform concurrent computations on large sets of historical data and projected variables to determine future risk probabilities. Parallel processing, coprocessor acceleration, high-performance data storage and streamlined memory I/O and throughput all play a role in enabling rapid simulation and reporting.