We'll explain how Brytlyt became the first vendor to use a GPU-Accelerated SQL database to run the TPC-H benchmark. TPC-H, a decision-support benchmark for a SQL database, consists of a suite of business-oriented ad-hoc queries using data with broad industry-wide relevance. We'll explain how it illustrates decision-support systems that examine large volumes of data, execute queries with a high degree of complexity, and give answers to critical business questions. We'll also discuss our Brytlyt GPU database and analytics platform, which is based on the open source PostgreSQL database.
Do you have a GPU cluster or air-gapped environment that you are responsible for but don't have an HPC background? NVIDIA DGX POD is a new way of thinking about AI infrastructure, combining DGX servers with networking and storage to accelerate AI workflow deployment and time to insight. We'll discuss lessons learned about building, deploying, and managing AI infrastructure at scale from design to deployment to management and monitoring. We will show how the DGX Pod Management software (DeepOps) along with our storage partner reference-architectures can be used for the deployment and management of multi-node GPU clusters for Deep Learning and HPC environments, in an on-premise, optionally air-gapped datacenter. The modular nature of the software also allows experienced administrators to pick and choose items that may be useful, making the process compatible with their existing software or infrastructure.
NVIDIA's DGX-2 system offers a unique architecture which connects 16 GPUs together via the high-speed NVLink interface, along with NVSwitch which enables unprecedented bandwidth between processors. This talk will take an in depth look at the properties of this system along with programming techniques to take maximum advantage of the system architecture.