53 citations · 197 across the 16 of their papers we have counts for
12 papers · 1 filter
JoinBoost: Grow Trees Over Normalized Data Using Only SQL
Zezhou Huang, Rathijit Sen, Jiaxiang Liu +1
Although dominant for tabular data, ML libraries that train tree models over normalized databases (e.g., LightGBM, XGBoost) require the data to be denormalized as a single table, m…
Revisiting Query Performance in GPU Database Systems
Jiashen Cao, Rathijit Sen, Matteo Interlandi +2
GPUs offer massive compute parallelism and high-bandwidth memory accesses. GPU database systems seek to exploit those capabilities to accelerate data analytics. Although modern GPU…
The Tensor Data Platform: Towards an AI-centric Database System
Apurva Gandhi, Yuki Asada, Victor Fu +6
Database engines have historically absorbed many of the innovations in data processing, adding features to process graph data, XML, object oriented, and text among many others. In…
Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem
Yuki Asada, Victor Fu, Apurva Gandhi +8
We demonstrate Tensor Query Processor (TQP): a query processor that automatically compiles relational operators into tensor programs. By leveraging tensor runtimes such as PyTorch,…
End-to-end Optimization of Machine Learning Prediction Queries
Kwanghyun Park, Karla Saur, Dalitso Banda +3
Prediction queries are widely used across industries to perform advanced analytics and draw insights from data. They include a data processing part (e.g., for joining, filtering, c…
Query Processing on Tensor Computation Runtimes
Dong He, Supun Nakandala, Dalitso Banda +7
The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the numbe…
Predictive Price-Performance Optimization for Serverless Query Processing
Rathijit Sen, Abhishek Roy, Alekh Jindal
We present an efficient, parametric modeling framework for predictive resource allocations, focusing on the amount of computational resources, that can optimize for a range of pric…
Optimal Resource Allocation for Serverless Queries
Anish Pimpley, Shuo Li, Anubha Srivastava +7
Optimizing resource allocation for analytical workloads is vital for reducing costs of cloud-data services. At the same time, it is incredibly hard for users to allocate resources…
A Comparative Exploration of ML Techniques for Tuning Query Degree of Parallelism
Zhiwei Fan, Rathijit Sen, Paraschos Koutris +1
There is a large body of recent work applying machine learning (ML) techniques to query optimization and query performance prediction in relational database management systems (RDB…
Lessons learned from the early performance evaluation of Intel Optane DC Persistent Memory in DBMS
Yinjun Wu, Kwanghyun Park, Rathijit Sen +2
Non-volatile memory (NVM) is an emerging technology, which has the persistence characteristics of large capacity storage devices(e.g., HDDs and SSDs), while providing the low acces…
Extending Relational Query Processing with ML Inference
Konstantinos Karanasos, Matteo Interlandi, Doris Xin +10
The broadening adoption of machine learning in the enterprise is increasing the pressure for strict governance and cost-effective performance, in particular for the common and cons…
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
Ashvin Agrawal, Rony Chatterjee, Carlo Curino +19
Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios inc…