CoddSpeed: Hardware Accelerated Query Processing in Microsoft Fabric
- Matteo Interlandi ,
- Nicolas Bruno ,
- Brandon Haynes ,
- Carlo Curino ,
- Rathijit Sen ,
- Yinan Li ,
- Kaushik Rajan ,
- Bailu Ding ,
- Lukas M Maas ,
- Wei Cui ,
- Kevin Gaffney ,
- Minsheng Hong ,
- Brian Kroth ,
- Sampath Rajenda ,
- Peng Cheng ,
- Surajit Chaudhuri ,
- Johannes Gehrke ,
- Raghu Ramakrishnan ,
- Lidong Zhou ,
- Momin Al-Ghosien ,
- Craig Peeper ,
- Marius Dumitru ,
- Conor Cunningham ,
- Kevin Bocksrocker ,
- Prasanna Sundar ,
- Ron Boskovic ,
- YongChul Kwon ,
- Marko Zivanovic ,
- Runbin Shi ,
- Krystian Sakowski ,
- Danis Lamtsov ,
- Josep Aguilar Saborit ,
- Krish Srinivasan ,
- Adarsh Kapil ,
- Andrew Putnam ,
- Anudeep Kambapu ,
- Aparna Konduri ,
- Aaron Landy ,
- Aaron Moore ,
- Aaron Pitman ,
- Artem Oks ,
- Ashit Gosalia ,
- Babu Kandimalla ,
- Blake Pelton ,
- Bogdan Crivat ,
- Cesar Galindo-Legaria ,
- Chidamber Kulkarni ,
- Jesús Camacho-Rodríguez ,
- Evgeny Babin ,
- Hans Lehnert Marino ,
- Igor Konev ,
- Israel Arroyo ,
- Luis Penaranda ,
- Mandar Datar ,
- Morten Borup Petersen ,
- Nicholas Simmons ,
- Parminder Poonian ,
- Pravija Danda ,
- Rob Rydberg ,
- Esteban Calvo ,
- Ronak Bajaj ,
- Sai Sumanth Kammal Shetty ,
- Sharanya Bhat ,
- Zach Iracheta
SIGMOD 2026 Industrial Track |
Organized by SIGMOD
Over the past three decades, Microsoft has developed several multi-billion-dollar data management products. We have now unified them into Microsoft Fabric, a comprehensive SaaS suite for data management based on a modern cloud-native design that delivers best-in-class analytics performance on CPUs. However, Microsoft’s AI investments have transformed our data centers. Hardware accelerators are now plentiful and surpass traditional CPU servers by orders of magnitude in compute, memory, and networking capabilities. Running analytics on hardware accelerators is therefore a business imperative. In this paper, we describe a multi-year effort to take Fabric to the next level by enabling analytics execution on state-of-the-art hardware accelerators. Given the rapid evolution of hardware, we have made hardware independence our guiding principle. We validate our architectural flexibility by demonstrating how we can run Fabric engines (e.g., Data Warehouse) on a variety of compute and network accelerators (e.g., GPUs, FPGAs, ASICs, NVLink, InfiniBand). We present our most mature implementation: a GPU-based execution engine derived from our Tensor Query Processor (TQP) and report key results from a novel data movement system leveraging NVLink and InfiniBand. This version of Fabric outperforms its CPU counterpart by over an order of magnitude across a host of production and benchmark scenarios (e.g., delivering up to 30x on TPC-H 1TB). We share the lessons learned in building one of the first systems in the era of accelerated analytics.