Low-level tuning

Mostak and his colleagues have fine-tuned the MapD platform by caching active data in GPU database memory, compiling queries on the fly using the Low-Level Virtual Machine (LLVM) framework and creating a system that can support vectorised queries when possible.

MapD’s product is a columnar database specifically tailored to run SQL queries in parallel across GPU cores. The object is to deliver immediate visual insights into complex data sets, according to Mostak, who now serves as MapD CEO. He said GPUs serve both to analyze the data and to render it for users’ viewing.

The work on the SQL column database that underlies the system began at MIT, where Mostak had gone to join the Computer Science and Artificial Intelligence Laboratory, working with noted database engineer, Michael Stonebraker.

“I realized computer science might be a better fit for my interests,” Mostak said.

Data in, insight out

An early adopter described the MapD package as a combination of visualization and processing power especially suited for GPUs. Abdul Subhan, a principal architect at Verizon Communications Inc., suggested MapD could be useful in “any use case where you have tremendous amounts of data, but need an answer fast.” He estimated that the product can perform a 3.2 billion-row data set query in milliseconds.

Subhan’s present use cases range from network operations to tracking status of software updates on devices, although he contemplates future uses in ad campaign tracking, as well.

“The database is fast, because it is using the true power of the GPUs, so the data is available almost immediately to the processors,” he said.

He indicated that MapD’s SQL interface had advantages compared to Hadoop-based products, as the latter require very specific programming skills and knowledge of programming languages. By comparison, MapD’s front end supports typical data load styles that should be familiar to working database administrators and sys admins. Subhan evaluated the product with an eye toward cost per unit of power and space consumed vs. query speed. Overall, “it’s a small footprint,” he said, suggesting that configuring GPUs in 2U servers can significantly reduce hosting requirements.

Analyst group Gartner has given good grades to MapD, as well, including the company in its list of ”Cool Vendors in DBMS, 2016.” In the report, Gartner analyst Nick Heudecker said users looking for systems with situational awareness in the face of quickly arriving data should consider this GPU database. At the same time he noted challenges that MapD faces as it reaches into organizations unfamiliar with GPUs.