Talk Title: Fourth Generation Compute @ Two Sigma

Talk Abstract: Two Sigma’s compute and computational requirements have grown dramatically over the past two decades and this growth is expected to be supercharged with our embrace of all sorts of AI models. This talk covers how our supercomputing design principles have evolved over the last two decades; from a simple scheduling system managing on-premise computers, to a network of purpose built, workflow aligned compute clusters capable of training ML models using petabytes of memory and thousands of GPUs that push the edges of training machine learning models with time series data. The talk raises several open questions, challenges and highlight areas where a collaboration with MOC-A can help manage ML at scale, especially as applied to quantitative finance.  

Bio: David Palaitis is technology leader working at the intersection of advanced machine learning, quantitative finance and high-performance computing. At Two Sigma, he focuses on building out large scale AI & Machine Learning Systems to accelerate throughput of model development at scale.