JDA Labs recently held their first meet-up of 2018 around “The Art of Data Visualization.” The meet-up featured speakers from JDA Labs and MapD.
Guidelines for Data Visualization
Ignacio Alvarez from JDA Labs spoke about guidelines for data visualization. As technology is using more and more data and artificial intelligence (AI) to drive business, people tend to forget how much this information affects users. Although visual representation is already commonly used to display and visualize data, it’s still very important to come up with the best way to present it. From an AI perspective, we need to be thinking about how to make the results trustworthy. Authors in the data visualization field have written guidelines on how to be effective with your visualization choices. The presentation summed up specific guidelines into six rules you can follow to make the best data visualization choices possible.
Large-scale GPU-Accelerated Data Visualization with MapD
Aaron Williams and Christophe Viau from MapD spoke about large-scale GPU-accelerated data visualization with MapD. Nearly a decade ago, disk-based data analytics platforms began to be superseded by in-memory systems, which offered orders-of-magnitude more bandwidth than their predecessors. This technological sea change was driven in large part by memory prices falling to the point where it became viable to hold large working sets of data entirely in RAM.
Today we’re about to witness a similar paradigm shift as analytics workloads are increasingly shifted from CPUs to GPUs, which possess much higher compute and memory bandwidth than CPUs. Driven by the needs of 4K gaming and deep learning, GPUs are just now beginning to have enough onboard RAM to cache meaningful sized datasets. Today, 8 GPUs and 256GB of GPU VRAM can be fit into a single server, and those numbers will likely rise significantly in the near future. And, while CPUs have seen relatively minimal memory bandwidth increases over the last several years, GPUs are rapidly moving to stacked DRAM (high-bandwidth memory), meaning by next year a single GPU will possess over a terabyte per second of bandwidth.
Using the MapD big data analytics platform as an example, Aaron Williams and Christophe Viau explained why analytics platforms that will be able to leverage GPUs will have an immense advantage over their CPU-bound counterparts. They showed how MapD leverages the massive parallelism and memory bandwidth of multiple GPUs to execute SQL queries and render complex visualizations of billions of rows in data in milliseconds, literally orders of magnitude faster than CPU systems. Finally, they showed why this difference matters, highlighting the potential of GPU-based analytics to allow truly interactive exploration of big datasets.
Download the slides from the presentations here.
About JDA Labs
Ever wonder what the supply chain of the future will look like? What technology advancements will reshape the way we do business? We do, every day.
Looking toward the future is the job of JDA Labs, a global research and development team headquartered in Montreal, Canada. Every day, the creative thought leaders at JDA Labs are focused on delivering game-changing new products, winning patents and developing revolutionary best practices that will shape the future supply chain — and deliver incredible results for our customers. Learn more about JDA Labs here.