Distributed Compute Labs is a Canadian educational nonprofit organization responsible for developing the next-generation of computing networks.
Modern day research requires extensive computing power. Researchers are competing for limited resources, either in availability or cost. Meanwhile, your computer's own processing power is going to waste while your computer sits idle. Distributed Compute Labs is building the tools for you to provide your computing power to the network as well as use computing power from the network on demand. Organizing processing power as a utility will accelerate compute-enabled research, innovation, and discovery across the world.
Computing resource suppliers receive Distributed Compute Credits (DCC) in exchange for their computing time. The protocol accesses unused cycles in otherwise idle machines or completely uses dedicated machines. DCC can then be used to deploy new computing tasks, or they can be sold in the global marketplace.
Researchers and developers deploy computational projects in exchange for DCC. Abundant compute resources are available at a fraction of the cost of current commercial cloud computing services, disrupting existing market powers and accelerating compute-enabled research, innovation, and discovery.
Exact mathematical solutions describing the electromagnetic force interactions arising between a two-layer ferromagnetic conducting tube and a coaxial bobbin coil, carrying a DC or AC current and traveling at constant velocity, are presented here. The research explores innovative electromagnetic propulsion technologies that have potential applications in futuristic transportation such as magnetic levitation and space elevators.
To simulate different geometries and material properties would require some 109 computations which would take almost two years to complete using a single modern CPU. By spreading these calculations over a million CPUs on the DCP network, the task is completed in hours instead of years.
Connect idle processing power - from mobile devices to mainframes - to help accelerate academic, research, institutional, and enterprise computational objectives.
Deploy compute tasks from your computer and obtain results at unprecedented speed, cost-effectiveness, and ease of use. Compute resources are securely obtained from a decentralized compute supply.
Dr. Kristine Spekkens
Associate Professor, RMC Physics and Space Science
Dr. Gunnar Blohm
Associate Professor, Queen’s University Computational Neuroscience
President & CEO Compute Ontario
Dr. Felicia Magpantay
Assistant Professor, Queen’s University Mathematics and Statistics
DCP consists of a task scheduling system, an algorithm for efficiently distributing those tasks, and a compartment to securely compute them. Research groups can compute together using their own equipment, and reach out to a trusted network of global providers during peak demand.
DCP remains browser-compatible, enabling new opportunities in distributed computing. When embedded, micro-computations provide passive income from computing power; these provide welcome alternatives to webpage advertisements or subscription costs.
Subsequent versions of the protocol will see the addition of more extensive implementation kits and auxiliary libraries that will plug into widely used and industry-leading software.
DCP is standards-based and future-proof.
It is lightweight and simple in form, built on top of standards that share a similar philosophy.
WebAssembly was created for optimal performance without compromising security. Specific tasks can be hard-coded and compiled ahead of time, allowing developers to benefit from the convenience and performance of both high- and low-level languages. WebAssembly is a compile target for many native languages including C, C++, Julia, and Rust. Dozens of other compilers that have WebAssembly as a compile target are under development by their respective communities.
DCP currently integrates WebGL 2.0 for rendering and general computing tasks. WebCompute Shaders, the browser equivalent of CUDA, is under development. Many home computers have GPUs that can be accessed by DCP, representing in aggregate more computing power than centralized GPU centers. DCP will enable GPU processing and in the future, general-purpose computing on graphics (GPGPU) computation, parallel data and algorithms, physics simulations, machine learning, and neural networks.