Global Research Runs on DCP
Whether you are a college student or an international taskforce, DCP makes collaborating on compute easier than ever before. Besides speeding up your workloads, there are multiple benefits to using DCP:
Share your research as gorgeous websites with interactive parameters and native compute. Compatible with CSS themes and more, making science interactive has never been this easy.
DCP apps implement deterministic math libraries. For the first time, your audience can back-check computational results and get the same answer regardless of their operating system.
Share your underutilized compute with colleagues around the world, or leverage open university grids. Leading institutions are connecting machines with the Distributed Computer to accelerate science & innovation.
The first user of DCP, Dr. Daniel Desjardins, had compute-heavy research requirements in electrodynamics. His work involved differential equations and mathematical solvers.
Listen to Dr. Desjardins explain how the protocol has accelerated his research 100x compared to mainstream tools.
DCP is ideal for the kinds of data parallel workloads common in research computing today, such as:
The compute needed for AI is doubling every 3.5 months. Fortunately, most of the fundamentals like hyperparameter searching are parallel and can be turbocharged with DCP.
DCP is completely device agnostic, making it the ideal tool for lightweight IoT deployments. It can make use of compute on any network, including 5G.
From BLAST searches and genetic algorithms to protein folding and genome sequencing, DCP cuts down the time to novel insights.
From securities analysis to tracking the market, massive compute power can be an invaluable tool in financial analysis. DCP accelerates it all.
Many of the most common methods in mathematics are well suited to parallel execution, from finite element analysis to partial differential equations.
Monte Carlo analysis is a perfect application for DCP, as are other stochastic simulation methods. Many more types can be modelled and run as parallel components.
Computational science is tough enough without having to manage Containers and VMs The Compute.for( ) function abstracts away the tedious parts, so you only have to worry about your own code.
Unlike other
platforms, DCP
lets you spend less time worrying about technical difficulties and more time making breakthrough discoveries!
Unfortunately, many people with brilliant ideas for cutting edge research are held back by a lack of resources. Distributed Compute Labs works with its partners to scavenge compute and donate it to ambitious projects.
As a community, DCL wants to see a world where no good idea has to be scaled back because of a lack of hardware. If you need help, contact us today!
Built on open source technology, learn how DCP builds superclusters out of heterogeneous machines.
The global network powered by DCP is an abundant pool of computing power. Learn how it can help your workload, and how to join.