Industry 4.0 and the trend towards the industrial internet of things is placing new demands on enterprise compute infrastructure. The Distributed Computer is a complete, secure platform for managing some of the most compute-intensive workloads.
Data streams may be pre-processed on-site for security and fast reaction times.
Improve lean manufacturing and costs by predicting sub-par components early on.
Monitor quality assurance and model final acceptance test failures.
Implement cutting-edge AI and Edge ML solutions at low cost.
Global health R&D and patient care relies on increasingly compute-intensive methods. The Distributed Computer provides low-cost, high throughput workloads while complying with regulations and data privacy standards.
Stochastic and deterministic models for predicting disease transmission.
Fast and cost-effective genome sequencing for personalized medicine.
Federated machine learning models from individual monitoring devices.
Secure compute intranets between hospitals, labs, and universities.
The Distributed Computer is an ideal tool for geospatial analysis. From pre-processing in space to compute-intensive workloads back on Earth, it makes insights from large datasets easy to capture and share.
Apply complex analysis to millions of images.
Integrate geospatial information with datasets from other disciplines.
Build custom web portals for users who provide their own compute.
With increasingly diverse hardware and networks, the IoT faces a severe integration challenge. By abstracting these away for the developer, the Distributed Computer lets applications tap into a smart grid of virtual infrastructure. In this way, every endpoint may provide both data and compute.
Monitor and optimize energy use with localized compute resources.
Track congestion from surveillance cameras with cutting-edge machine vision.
Build federated ML models from local data to improve city operations.
Quickly establish hyper-local compute instances, powered by any edge devices.
In addition to algorithmic trading, the financial services industry is increasingly making use of batch analyses and AI. Applications that make use of massively parallel compute providers portfolio managers, FIs, insurance firms, and more with a tremendous competitive advantage.
Data mining to inform decision making and predicting outcomes.
Test and deploy ML models for accurate, low cost actuarial functions.
Develop and back-test proprietary algorithms completely on-premise.
Gather insights from application users without compromising privacy.
Today, large amounts of compute are critical for achieving innovation. The Distributed Computer lets researchers process massive amounts of data at an acceptable cost and reduce time by an order of magnitude.
Kings works with Distributed Compute Labs as a strategic partner to provide subsidized resources to educational institutions and other non-profits. To acquire these, please visit their website.
Create a private cloud for R&D from underutilized, idle hardware.
Iterate on convolutional neural networks and other advanced ML models.
Perform exhaustive Monte Carlo analysis and other simulations.
Discover new advanced materials through ab initio high throughput screening.
If your industry is not listed, please reach out to our team of experts. The Distributed Computer is always finding new applications.