[DB Seminar] Fall 2018: System Design Planning

More design discussion of the new DBMS. Read More

[Hardware Accelerated Databases] Felipe Aramburu (BlazingDB)

BlazingDB has spent the past six months working on an open-source project (libgdf) alongside Anaconda and Nvidia. Libgdf is a library of computational primitives on top of a memory layout which is similar to Apache Arrow but optimized for GPUs. We have created a distributed, GPU-accelerated ETL pipeline that takes a user from reading data in Parquet, to performing SQL operations over that dataset, and finally feeding that data into xgboost, a machine learning library that allows us to leverage Read More

[DB Seminar] Fall 2018: System Design Planning with Prashanth Menon

This meeting will be entirely about discussing the design and implementation of CMU's new DBMS. The focus will be on how we combine the new storage manager with the beautiful Prashanth Menon's LLVM execution engine. Read More

Hardware Accelerated Database Lectures – Seminar Series (Fall 2018)

The CMU Database group is holding a semester-long seminar series with the leading developers of DBMSs designed for modern hardware (e.g., GPUs, FPGAs, ASICs). The Hardware Accelerated Database Lectures is designed to showcase some of the newer technologies available for data-intensive applications. Each speaker will present the implementation details of their respective systems and examples of the technical challenges that they faced when working with real-world customers. The list of confirmed speakers are: Sep 6 - Nima Negahban (Kinetica) Sep Read More

[Hardware Accelerated Databases] Jake Wheat (SQream)

This talk will present SQream's journey to building an analytics data warehouse powered by GPUs. SQream DB is an SQL data warehouse designed for larger than main-memory datasets (up to petabytes). It's an on-disk database that combines novel ideas and algorithms to rapidly analyze trillions of rows with the help of high-throughput GPUs. We will explore some of SQream's ideas and approaches to developing its analytics database – from simple prototype and tech demos, to a fully functional data warehouse Read More

[Hardware Accelerated Databases] Nima Negahban (Kinetica)

Widespread digital transformation and the explosion of the Internet of Things has led to a dramatic increase in the volume, complexity and unpredictability of data. This has created a need for converged solutions that require a new combination of processing capabilities that are not met by traditional OLTP and OLAP implementation approaches. The challenge set by modern enterprise is to create a scalable converged platform that can simultaneously manage millions of mutations, power high throughput key lookup, do high resolution Read More

[DB Seminar] Fall 2018: Andy Pavlo

Andy will regale everyone with stories from the summer and CMU-DB research plans for the next year. Read More

Molham Aref (Relational AI)

In this talk, I will summarize our work on in-database solvers for relational machine learning and artificial intelligence. We implement a declarative query language that offers support for model (or instance) finding. The capability is used to express predictive and prescriptive analytics. The presentation gives an overview of the platform and the language. In particular, it focuses on the use of algebraic (e.g. semi-rings) and combinatorial structure (e.g. query width) for asymptotic improvements of gradient descent based solvers used in Read More

[Hardware Accelerated Databases] Todd Mostak (MapD)

In this talk Todd Mostak, Founder and CEO of MapD Technologies, will speak about the technical underpinnings of MapD, an open-source GPU-accelerated SQL engine and visual analytics platform that enables querying and visualization of large datasets (tens of billions of records) in milliseconds, without indexing or or other forms of pre-computation. While some of this performance can be attributed to the system’s use of GPUs, a large part is also due to a focus around extracting the full performance and Read More

Pat Helland (Salesforce)

Applications have had an interesting evolution as they have moved into the distributed and scalable world. Similarly, storage and its cousin databases have changed side by side with applications. Many times, the semantics, performance, and failure models of storage and applications do a subtle dance as they change in support of changing business requirements and environmental challenges. Adding scale to the mix has really stirred things up. This article looks at some of these issues and their impact on systems. Read More