Global STAC Live, Fall 2020

STAC Summits

Following our highly attended online event in the spring,
STAC is going worldwide, on-line, and live again this fall.

Join us for live panels, presentations, and exhibits that address the most important technical challenges in analytics technology, low-latency infrastructure, and command & control. Interact directly with speakers, exhibitors, and your peers – all without leaving your home or office.

(These are topics, not necessarily sessions. Details will follow shortly.)
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Day 1: Time Series STAC
2020-10-19 12:30:00

Experts discuss leading ways to ingest, store, retrieve, and analyze live and historical time series data. Topics include:

  • What the future holds (or should hold) for time series analysis stacks
  • A new database benchmark suite for streaming data
  • Streaming analytics on data from the real economy (IT telemetry, IoT)
  • Realtime data visualization
  • New results from benchmarks on historical time series data
  • The latest software and hardware innovations for time series stacks

Stay tuned for session announcements.

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Day 2: Real Time STAC
2020-10-20 12:30:00
An FPGA primer for software developers
  • Dr Matthew Grosvenor, Principal Engineer, Cisco

As FPGAs become part of the standard kit for an expanding range of low-latency, high-throughput applications, more and more software developers find themselves wanting (or needing) to develop FPGA logic to stay relevant. But circuit-based hardware is a whole new universe to someone with a career spent in an instruction-based world. Can a software developer understand how to program FPGA? Matthew thinks so. He has spent over a decade working in both worlds and thinks the key to FPGA is understanding some basic concepts. In this talk, Matt will offer software developers a jumping-off point by explaining how FPGAs work, how to make sense of FPGA products, and how FPGA programming languages reflect the underlying hardware concepts.

STAC Fast Data Update
  • Peter Nabicht, Head of Strategy, STAC

Peter will announce the industry’s first FPGA Special Interest Group, a group of trading firms that will focus on common challenges in FPGA development, testing, and deployment.

Agile FPGA?
  • Dr David Snowdon, Director of Engineering, Arista

In the 20 years since the Agile Manifesto was signed, the way we’ve developed and deployed software code has improved by leaps and bounds. Features can get out the door quickly while remaining reliable. But what about FPGA code? Even though FPGA is a popular platform choice in finance, deployment and management of FPGA logic is mired in the past. Can we achieve the rapid iteration, fast delivery times, and increased stability of software, while maintaining the benefits of hardware acceleration? Dave thinks we can. In his view, we should start thinking of FPGAs as just another place to deploy software. Using real-world examples, Dave will discuss how we can adapt the FPGA development process to take advantage of modern software engineering techniques and dev ops processes.

Modern realtime data distribution
  • Peenaki Dam, Head of Product Innovation, Intercontinental Exchange
  • Sal Sferrazza, Solutions Architect, Financial Services, Google Cloud
  • Stefan Ott, Managing Director, CEO, Confinity Solutions GmbH
  • Charles Fan, co-founder and CEO, MemVerge
  • Andrew MacGaffey, President, MetaFluent

When messaging middleware for realtime data distribution first appeared, the main mission was to get event-driven data from a small number of servers to a large number of trader desktops or gateways in "real time". All the machines were on site and had one or two processors. All the middleware was proprietary. "Real time" meant sub-second. Since then, a lot has changed. Humans consume data across a wide variety of devices and networks, and algorithms consume far more data than humans. "Real time" now ranges from sub-second to sub-microsecond. And producers or consumers of data may be in a public cloud. Our panel of experts will help us make sense of today's middleware situation and where it's headed. Given the latest advancements, how should a CTO, architect, or app developer think about what messaging platform is best for a given realtime problem? Where do requirements between use cases overlap, and what does that mean for integration of messaging systems? What role do open source products have to play? How does cloud change the picture as a deployment context or delivery vehicle? What is the interplay of messaging platforms with innovations in compute, networking, and memory?

Innovation Roundup
  • Lineup to be announced shortly

Leading vendors will present FPGA-related innovations.

Additional topics to be announced

Discussions in real time communication and action.

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Day 3: Big Workload STAC
2020-10-21 12:30:00
A sharper Arrow
  • Wes McKinney, Director, Ursa Labs

Apache Arrow is a cross-language development platform for in-memory columnar data processing. Among other things, it contains a language-independent columnar data format designed for fast transport and processing on modern hardware. Now in the top 15 most active Apache projects, Arrow has over 500 contributors and over two million weekly downloads. With the Python and R communities using it to facilitate CPU and GPU computations, as well as uptake by major cloud data warehouses, Arrow has a shot (pun intended) at becoming a universal “native” format for data-intensive computing. As a co-creator of Arrow and a key member of its Project Management Committee, Wes will provide a progress update with particular focus on enhancements that can benefit high-throughput data services, data exchange in distributed systems, and C++ analytics on time series, both in-memory and out-of-core. Come to get up to date and ask Wes your questions.

The trader of the future: Smarter, faster, and even more Pythonic
  • Dr John Ashley, General Manager, Financial Services and Technology, NVIDIA

William Gibson famously said that "the future is already here--it's just not evenly distributed". In John's view, that maxim applies to trading desks as much as any other part of life. Some desks continue doing what has worked for the last decade, hand-crafting algorithms on a relatively narrow slice of market data in, say, Excel or C++. Another group is expanding their data sources and exploiting the benefits of the Python ecosystem, including ML/AI tools. John will argue that the future belongs to the latter. He will explain how Python makes it easy for traders to create smarter strategies, and he'll offer tips on how to get smarter faster. By smarter, he means taking in more contextual data and exploiting the latest ML techniques, which he'll demonstrate by showing how a BERT-class model can turn natural language into insights. By faster, John means both faster to market and faster in the market. To accelerate strategy development and backtesting, he will share some best practices and Python community resources. To get ML models to react more quickly to markets, John will discuss the importance of optimizing inference and how to do it. Come to see this view of the future and ask your questions about it.

Benchmarking realtime LSTM inference on time series
  • Michel Debiche, Director of Analytics Research, STAC

Many of the performance challenges associated with machine learning (ML) are in the training phase: that is, model development. But for certain use cases like time-sensitive trading, the inference phase (the application of a model to new data) also has its challenges. For example, if a model provides realtime input to trading algorithms--or perhaps even does the trading itself--inference latency may be a prime consideration. And if the model is deployed in a resource-constrained environment like a colocation center, throughput per unit of resource is probably also a concern. In this talk, Michel will present proposed STAC benchmarks for realtime inference, using an example with long short-term memory (LSTM) networks. He will argue that benchmarks of inference latency will not only inform sizing decisions for well-established ML techniques but will also be a key factor in deciding whether less proven ML techniques can or cannot be applied effectively to various financial use cases.

Additional topics to be announced

Discussions in HPC, data science, and associated tooling.


To see agendas from past STAC Summits, click here.
If you have suggestions for this agenda, please contact us.

About STAC Events & Meetings

STAC events bring together CTOs and other industry leaders responsible for solution architecture, infrastructure engineering, application development, machine learning/deep learning engineering, data engineering, and operational intelligence to discuss important technical challenges in trading and investment.

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