STAC Summit, 19 Oct 2022, NYC

STAC Summits

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STAC Summits 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.




Partial Agenda



STAC Update: Machine learningMachine Learning   
 
  • Bishop Brock, Head of Research, STAC

Bishop will discuss the latest research and Council activities related to machine learning, including updates on inference and training workloads.

Optimization strategies for the DL storage stackMachine Learning   
 

  • Sven Oehme, Chief Technology Officer, DDN

Many capital markets technologists have experience optimizing storage architectures for workloads like time-series databases and backtesting. But the training phase of Deep Learning is a different workload, with a complex mix of high-concurrency reads, checkpointing, and random large-block mmaps. The storage stack (client, network, file system, and storage hardware) must cater to these access patterns during initial model training as well as retraining. Otherwise, I/O bottlenecks can leave costly compute resources underutilized and delay model deployment. (Few storage architects want to be responsible for missed business opportunities.) In this talk, Sven will help you design more efficient AI solutions by exploring what happens during DL training from the storage system's point of view. Using lessons from real-world examples, he'll discuss the implications for the storage stack and walk through optimizations for the file system and the rest of the datapath.

Additional machine learning sessionsMachine Learning   
 
  • Topics and speakers to be announced
STAC Update: Big workloadsBig Data   Big Compute   
 
  • Peter Nabicht, President, STAC

Peter will present the most recent results from enterprise tick analytics and market risk.

Additional big workload sessionsBig Data   Big Compute   
 
  • Topics and speakers to be announced
STAC Update: Fast dataFast Data
 
  • Peter Nabicht, President, STAC

Peter will discuss the latest research and Council activities related to low-latency/high-throughput realtime workloads.

Accelerating "time to first trade" with FPGAsFast Data
 

  • Mark Eslinger, Senior Principal Product Engineer, Digital Verification Technologies, Siemens

As trading strategies requiring ultra-low latency have become more complex, so have the designs firms are implementing in FPGAs. In fact, design size and complexity are outpacing headcount – a recipe for stalled projects, underperforming solutions, and buggy implementations. At a time when it is critical to quickly adapt to fast moving markets, hardware engineers must find a way to step on the gas. The software world relies heavily on continuous integration (CI) to get to market faster, but the approach is still underutilized in hardware. In this talk, Mark will lay out a faster path to “go live” via an easy-to-adopt methodology that maximizes CI throughput, exploits automated, exhaustive RTL code inspection, and assures code quality throughout the development process. Join him to learn ways to accelerate your FPGA projects’ time to first trade.

Exploring the latest advances in market connectivity and data servicesFast Data
 
  • Details to be announced

Advancing the open source ecosystem for hardware developmentFast Data
 
  • Details to be announced

Additional fast data sessionsFast Data   
 
  • Topics and speakers to be announced
Innovation RoundupsMachine Learning   Fast DataBig Data   Fast Compute   Big Compute   
 
  • Full lineup to be announced shortly

A growing list of vendor presentations from firms including Achronix, AMD, Arista, Dell, Exegy, Groq, LDA Technologies, OFS Fitel, Options, Orolia, and Starfish Storage.

 

~5:00pm Networking Reception

 

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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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