The internet has changed how people consume media. Rather than just watching television and movies, the combination of ubiquitous mobile devices, massive computation, and available internet bandwidth has led to an explosion in user-created content: users are recreating the internet, producing exabytes of content every day.
This massive scale of content requires massive amounts of processing, and due to the volume of media content involved, data center workloads are changing. Increasing resources are spent on video and image processing, resizing, transcoding, filtering and enhancement. Likewise, large-scale machine learning and deep learning techniques apply trained models to what’s known as “inference”, which applies trained models to tasks such as image classification, object detection, machine translation, and speech recognition.
You may already know NVIDIA Tesla as a line of GPU accelerator boards optimized for high-performance, general-purpose computing. They are used for parallel scientific, engineering, and technical computing, and they are designed for deployment in supercomputers, clusters, and workstations. But it’s not just the GPU boards that make Tesla a great computing solution. The combination of the world’s fastest GPU accelerators, the widely used CUDA parallel computing model, and a comprehensive ecosystem of software developers, software vendors, and data center system OEMs make Tesla the leading platform for accelerating data analytics and scientific computing.
The Tesla Accelerated Computing Platform provides advanced system management features and accelerated communication technology, and it is supported by popular infrastructure management software. These enable HPC professionals to easily deploy and manage Tesla accelerators in the data center. Tesla-accelerated applications are powered by CUDA, NVIDIA’s pervasive parallel computing platform and programming model, which provides application developers with a comprehensive suite of tools for productive, high-performance software development.
This post gives an overview of the broad range of technologies, tools, and components of the Tesla Accelerated Computing Platform that are available to application developers. Here’s what you need to know about the Tesla Platform. Continue reading →
NVIDIA GPU Boost™ is a feature available on NVIDIA® GeForce® products and NVIDIA® Tesla® products. It makes use of any power headroom to boost application performance. In the case of Tesla, the NVIDIA GPU Boost feature is customized for compute intensive workloads running on clusters. This application note is useful for anyone who wants to take advantage of the power headroom on the Tesla K40 in a server or within a workstation. Note that GPU Boost is a system setting, which means that this Pro Tip applies to any user of a CUDA-accelerated application, not just developers.
The Tesla K40 board targets a specific power budget (235W) when running a highly optimized compute workload, but HPC workloads vary in power consumption and profile, as the graph in Figure 1 shows. This shows that for many applications there is power headroom. NVIDIA GPU Boost for Tesla allows customers to use available power headroom to select higher graphics clocks using NVML or nvidia-smi.
A great post by Saad Rahim on the Acceleware Blog covers everything you need to know to use GPU Boost. In the post, Saad benchmarks two applications with varying clocks on K40: Reverse Time Migration (RTM), a depth migration algorithm used to image complex geologies; and a Finite-difference time-domain (FDTD) electromagnetic solver. Continue reading →