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DDN Raises the Bar in High-Frequency Trading

DDN Raises the Bar in High-Frequency Trading
AI400X2 Delivers Unprecedented STAC Benchmark Performance Results

DDN, the global leader in data management solutions for artificial intelligence (AI), high performance computing (HPC) and multicloud, announced dramatically improved performance in the STAC-M3 Benchmark utilizing its newly announced A3I® AI400X2 appliance. Financial services firms looking to accelerate analytics can create more sophisticated trading models with this new platform.

These new benchmark results, audited by STAC® Research, show a single DDN appliance delivering high performance and throughput using a shared filesystem. While DDN A3I storage systems can scale without limit to increase capacity and throughput, these results demonstrate extreme performance in a cost-effective form factor. And, the A3I AI400X2 appliance has a smaller footprint, less complexity and lower overall TCO than competitive storage solutions.Prediction Series Banner

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The STAC-M3 is the financial services industry’s standard benchmark test suite for tick analytics. These benchmark specifications are maintained by the STAC Benchmark Councilâ„¢, which consists of over 450 financial institutions and vendor organizations, whose purpose is to discuss technical challenges and solutions in financial services and develop technology benchmark standards that are useful to financial organizations. Many participating firms inform important technology decisions with STAC-M3 results, including the largest global banks, brokerage houses, exchanges, hedge funds, and proprietary trading shops.

The benchmark test was performed on a single DDN A3I AI400X2 all-flash NVMe appliance connected to 15 servers running the KX Systems kdb+4.0 database in distributed mode via a high-speed InfiniBand switch.1 After completing its previous STAC benchmark tests in September 2020,2 DDN has since released the new AI400X2 appliance and the underlying file system, EXAScaler® 6, both of which were utilized in this year’s tests. With these improvements, DDN was able to reduce the number of appliances in the test, while still exceeding 10 of 17 baseline (Antuco) benchmark tests and 19 of 24 scaling (Kanaga) benchmarks, compared to the previous tests using two DDN AI400X appliances.

“Simplifying the management of data-intensive workloads and delivering faster insights to our customers is the basis of our development here at DDN,” said Kurt Kuckein, vice president of marketing at DDN. “These STAC benchmark results reflect the progress we’ve made toward streamlining data management while significantly accelerating analytics and AI workloads – particularly for our financial services customers.”

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Other notable DDN results from this STAC testing:

  • DDN outperformed a solution using kdb+ 4.0, three NFS-based network-attached flash storage systems and nine database servers3
    (DDN was faster in 11 of 17 mean response time Antuco benchmarks, including a 11.4x speedup in single-user intervalized statistics, STAC-M3.β1.1T.STAT-UI.TIME)
  • DDN also outperformed a solution using kdb+ 4.0, a parallel file system, 15 database servers, and 40 storage servers on a public cloud4
    (DDN was faster in 12 of 17 Antuco benchmarks, and 12 of 24 Kanaga benchmarks, including a 16.2x to 19.9x speedup in the four 10-user market snapshots, STAC-M3.β1.10T.YR{2,3,4,5}-MKTSNAP.TIME)

Like many of DDN’s enterprise customers, financial services firms are looking to modernize their business models with analytics and AI to generate new opportunities from their data. Existing approaches to data management using NFS-based configurations are often complex and inefficient, blocking the path to value because they do not scale to the size of the challenges. DDN’s A3I solutions greatly simplify data management for advanced computing applications and give enterprise IT a platform for delivering high performance data services without compromise – for every workload and every user.

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[To share your insights with us, please write to sghosh@martechseries.com]

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