Faster FAST: multicore acceleration of streaming financial data

  • Virat Agarwal
  • David A. Bader
  • Lin Dan
  • Lurng-Kuo Liu
  • Davide Pasetto
  • Michael Perrone
  • Fabrizio Petrini
Special Issue Paper

DOI: 10.1007/s00450-009-0093-5

Cite this article as:
Agarwal, V., Bader, D.A., Dan, L. et al. Comp. Sci. Res. Dev. (2009) 23: 249. doi:10.1007/s00450-009-0093-5

Abstract

By 2010, the global options and equity markets will average over 128 billion messages per day, amounting to trillions of dollars in trades. Trading systems, the backbone of the low-latency high-frequency business, need fundamental research and innovation to overcome their current processing bottlenecks. With market data rates rapidly growing, the financial community is demanding solutions that are extremely fast, flexible, adaptive, and easy to manage. This paper explores multiple avenues to deal with the decoding and normalization of Option Price Reporting Authority (OPRA) stock market data feeds encoded with FIX Adapted for Streaming (FAST) representation, on commodity multicore platforms, and describes a novel solution that encodes the OPRA protocol with a high-level description language. Our algorithm achieves a processing rate of 15 million messages per second in the fastest single socket configuration on an Intel Xeon E5472, which is an order of magnitude higher than the current needs of the financial systems. We also present an in-depth performance evaluation that exposes important properties of our OPRA parsing algorithm on a collection of multicore processors.

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Virat Agarwal
    • 1
  • David A. Bader
    • 2
  • Lin Dan
    • 1
  • Lurng-Kuo Liu
    • 1
  • Davide Pasetto
    • 3
  • Michael Perrone
    • 1
  • Fabrizio Petrini
    • 1
  1. 1.IBM TJ Watson CenterYorktown HeightsUSA
  2. 2.College of ComputingGeorgia Institute of TechnologyAtlantaUSA
  3. 3.IBM Computational Science CenterDublinIreland

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